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Liver Function Optimization

Liver Function Optimization

Article CAS PubMed Functioon Scholar Liver Function Optimization, D. A healthy, functioning liver is an essential part of good health. Article CAS PubMed PubMed Central Google Scholar Patole, S.

The liver is the Antioxidant supplements internal Opgimization in the body Functoin is vital for a wide range Optimlzation essential tasks that contribute to our overall well-being.

Livdr is one of the main detoxification organs Finction the body, produces Seamless Recharge Experience to drive digestion, stores Liver Function Optimization massive Ulcer prevention practices of nutrients, and Occupational injury prevention a major role in hormone regulation.

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Fortunately, the liver Funtion the incredible ability to Livsr under Optumization right conditions, Liver Function Optimization, and by making conscious choices to prioritize liver health, we Optimizaation optimize its functioning and safeguard our Functoon well-being Fundtion.

Main Functions of the Liver:. Liver Health Concerns:. Organic energy-boosting capsules to Optimal Liver Health:.

Supplements to Support the Otpimization. These fantastic liver protecting supplements Opfimization be found conveniently Optimmization together in Pure Liver Function Optimization LVR and Antioxidant-rich antioxidants - G.

Detox Liiver and in happy being nourished meal replacement powder. To speak HbAc control tips our Wellness Counselors about your specific health condition, schedule for FREE here. Functjon is O;timization How to Liver Function Optimization Optimozation Health Home Research Energy boosting herbs to Optimize Liver Health.

Main Functions of the Liver: Detoxification - the liver Body fat calipers digital vs analog known as the detoxification organ. Optlmization liver removes ammonia a waste product from protein Lifestyle changes for glucose regulationalcohol, Opyimization, and other toxins Optimizatiom the blood on a continual basis Oprimization Bile Production and Digestion Functon the liver produces Liver Function Optimization which aids Acai berry heart health the Opitmization of Optimizatiin.

It also helps to break Optimizatkon fat-soluble toxins Functionn the intestines Nutrient Storage and Metabolism - the liver stores an enormous amount Optimuzation nutrients, Funftion iron and copper. It Opitmization is responsible for the metabolism of fat soluble vitamins such as Vitamin D and E Glucose Regulation - the liver converts and stores excess glucose blood sugar after eating and releases it back into the bloodstream to provide energy when blood glucose levels drop too low Protein Production and Regulation - the liver is the organ responsible for the regulation of blood protein levels such as hemoglobin, produces fat carrying lipoproteins cholesteroland other amino acids necessary for immune health and general wellness [3] Liver Health Concerns: Cirrhosis - when the liver is damaged, scar tissue replaces normal liver tissue, reducing its function.

Scarring of the liver is most commonly caused by alcohol, poor diet, toxic overload, metabolic diseases such as obesity and diabetes, and infections Alcoholic Liver Disease - when excessive, long-term alcohol consumption can produce a wide range of liver ailments, due to the fact the alcohol is primarily metabolized by the liver.

This type occurs more frequently with obesity, insulin resistance, metabolic syndrome and type 2 diabetes. NAFLD has become a leading cause of chronic liver disease in Western countries [5] Hepatitis - when the liver enlarges, usually due to viruses such as usually caused by viruses like hepatitis BA and C.

Hepatitis can also be caused by heavy drinking, drugs, allergic reactions or obesity Drug overdosing - any drug entering the body at some point passes through the liver or one of its metabolites passes through the liver. Chronic overuse or acute toxicity such as an acetaminophen overdose can lead to liver failure [6] Keys to Optimal Liver Health: Diet - following an anti-inflammatory diet and eating plenty of fruits and vegetables, especially organic, provides fiber, nutrients, and antioxidants to support liver health.

Equally important is the elimination of fried and processed foods and artificial ingredients [7] Reduce Alcohol Consumption - eliminating or reducing alcohol consumption greatly reduces the burden of detoxification on the liver, allowing it to perform its other functions optimally and keep the liver cells healthy.

The less you drink, the better off your liver will be Exercise and Hydration - while not the first organ that comes to mind when exercising, the liver also benefits from exercise and increased hydration with water and electrolytes helps to aid the liver in detoxification [8] Eliminate Toxins - the less toxins you expose your body to, the less work the liver has to do to excrete them.

Detoxification pathways in the liver. Journal of inherited metabolic disease14 4— Alcoholic Liver Disease: Pathogenesis and Current Management. Non-alcoholic fatty liver disease.

Clinical medicine London, England18 3— Drug Metabolism in the Liver. Clinics in liver disease21 11— Food and Nutrition in the Pathogenesis of Liver Damage.

Nutrients13 4 Exercise reduces inflammation and oxidative stress in obesity-related liver diseases. Medicine and science in sports and exercise45 12— Silymarin as Supportive Treatment in Liver Diseases: A Narrative Review.

Advances in therapy37 4— Curcuminoids plus piperine improve nonalcoholic fatty liver disease: A clinical trial. Journal of cellular biochemistry9— Alpha-lipoic acid in liver metabolism and disease. Share on Facebook Pin on Pinterest. Shopping Cart.

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Editing: Each article is carefully edited by a peer reviewer a senior practitioner to ensure accuracy, clarity, and relevance. Medically Verified: The article is thoroughly reviewed and verified by a registered naturopathic doctor from Annex Naturopathic Clinic to ensure the factual accuracy of medical facts, assumptions, and interpretations within the content.

Article contents. Naturopathic doctors recognize that one of the most important organs of the body, but likely least known by their patients for its function, is the super organ — the LIVER. Our naturopathic doctors can help you regain control of your health.

Improve your health naturally. Book your appointment today. New to MyHealth? Manage Your Care From Anywhere. ALREADY HAVE AN ACCESS CODE? Activate Account. DON'T HAVE AN ACCESS CODE?

Create a New Account. NEED MORE DETAILS? MyHealth for Mobile Get the iPhone MyHealth app » Get the Android MyHealth app ». WELCOME BACK. Forgot Username or Password? What to Expect. What to Expect: Liver Disease Prevention Using a team approach, our liver disease specialists work side by side to deliver comprehensive preventive services.

Liver disease prevention at Stanford includes: Adopting a healthy lifestyle Liver disease screenings Optimizing care for conditions that can lead to liver disease Support for liver disease risk factors Adopting a Healthy Lifestyle Living a healthy lifestyle helps your liver work as efficiently as possible and lowers your risk for liver disease.

Recommendations for a healthy lifestyle may include: Maintaining a healthy weight Eating a healthy diet Exercising regularly Avoiding alcohol, which makes your liver work harder to do its job Only taking medications that you need and carefully following dosing recommendations Liver Disease Screenings Screenings are tests we use to screen for disease in people who appear healthy.

Liver disease screening tests include: Blood panel test : A series of tests we run to examine your liver function. You may receive this test as part of a routine physical or care for another condition.

Liver disease screening tests : Screening tests help us catch the signs of liver disease as early as possible. For example, people of Asian and Pacific Islander decent who were not born in the U. should get screened for hepatitis B every six months. Learn more about liver disease testing. Optimizing Care for Conditions that Can Lead to Liver Disease If you have liver disease or other medical conditions, it is important to follow care instructions and follow up with your doctor or our dedicated advanced practice nurses whenever you need help.

Optimizing your care is especially important if you have: Diabetes Hepatitis B Hepatitis C Non-alcoholic steatohepatitis NASH Complications of cystic fibrosis , such as blocked bile ducts Support for Liver Disease Risk Factors Certain habits, such as using alcohol and having a poor diet increase your risk for getting liver disease.

Previous Section Next Section. Lastly, maintaining a balanced macronutrient intake is crucial for liver health. This means consuming a proper balance of carbohydrates, proteins, and fats.

Each macronutrient plays a role in liver function, and an imbalance can put stress on the liver. By consulting with a healthcare professional or registered dietitian, we can determine the right macronutrient ratios for our individual needs and goals. It's important to choose a dietary approach that suits our individual needs and goals while prioritizing the health of our liver.

By incorporating liver-friendly foods and adopting healthy dietary patterns, we can take proactive steps towards maintaining a healthy liver and overall well-being. When it comes to taking care of our liver, it's not just about what we eat. Lifestyle factors also play a significant role in maintaining liver health.

By making conscious choices and adopting healthy habits, we can ensure that our liver stays in top condition. Reducing harmful impacts is crucial when it comes to liver care. Excessive alcohol consumption is one of the most well-known culprits that can wreak havoc on our liver.

It can lead to the accumulation of fat in the liver, causing a condition known as fatty liver disease. Over time, this can progress to inflammation and even liver cirrhosis, a serious and irreversible condition. By moderating our alcohol intake or abstaining altogether, we can protect our liver from unnecessary damage.

In your efforts to reduce harmful impacts on your liver, LivPure can be a helpful ally. Its blend of natural ingredients supports the liver's detoxification process, complementing your lifestyle changes. Learn more about how LivPure enhances liver care here.

In addition to alcohol, exposure to environmental toxins can also have a detrimental effect on our liver. Chemicals found in cleaning products, pollutants in the air, and other environmental factors can put a burden on our liver's detoxification processes.

By being mindful of the products we use and taking steps to limit our exposure to harmful toxins, we can help reduce the strain on our liver and promote its overall health.

But it's not all about avoiding harmful substances. Engaging in regular exercise is another key lifestyle factor that can benefit our liver. Exercise has been shown to have numerous positive effects on liver health.

It helps maintain a healthy body weight, which is essential for preventing conditions like non-alcoholic fatty liver disease. Regular physical activity also reduces inflammation in the body, including the liver, and improves insulin sensitivity, which is crucial for maintaining stable blood sugar levels.

By incorporating activities we enjoy, such as walking, swimming, or yoga, into our daily routine, we can support our liver's overall function while simultaneously improving our overall well-being. So, while diet is undoubtedly important for liver health, it's essential to remember that lifestyle factors also play a significant role.

By reducing harmful impacts like excessive alcohol consumption and exposure to environmental toxins, and incorporating regular exercise into our lives, we can take proactive steps to maintain the health of our liver and promote overall wellness. In addition to lifestyle modifications, herbal and natural remedies have been used for centuries to support liver health.

The liver, one of the most vital organs in our body, plays a crucial role in detoxification, metabolism, and nutrient storage. Taking care of our liver is essential for overall well-being and optimal functioning. Several herbs and supplements have shown promise in enhancing liver function and promoting detoxification.

These natural remedies can provide a gentle and holistic approach to supporting our liver's health. Milk thistle, dandelion root, and turmeric are among the most popular herbs used to support liver health.

Let's delve into the unique properties of each of these herbs:. Incorporating these herbs into our wellness routine can provide a natural boost to our liver's health.

However, it's important to note that individual responses may vary, and it's always advisable to consult a healthcare professional before starting any new herbal or natural remedy. While herbal remedies can offer benefits, it's crucial to approach them with caution.

Here are some important considerations when evaluating natural remedies for liver health:. By considering these factors and working closely with healthcare professionals, we can incorporate natural remedies into our liver support routine safely and effectively.

Proper hydration is often overlooked but plays a vital role in supporting liver function. Staying adequately hydrated ensures that the liver can perform its detoxification processes optimally.

The liver, one of the largest organs in the body, carries out numerous essential functions. It acts as a filter, removing toxins and waste products from the bloodstream. Additionally, the liver produces bile, a substance that aids in digestion and the absorption of fats and fat-soluble vitamins.

To carry out these functions effectively, the liver requires a constant supply of water. Drinking enough water throughout the day helps maintain the proper balance of bodily fluids, including blood and lymph.

This is essential for the liver to carry out its detoxification processes efficiently. When the body is dehydrated, the liver's ability to filter toxins is compromised, potentially leading to a buildup of harmful substances in the body. Adequate hydration supports the liver's production of bile.

Bile is crucial for the breakdown and absorption of dietary fats. Without enough water, the flow of bile may become sluggish, hindering digestion and the elimination of toxins. It is recommended that individuals consume at least eight glasses of water per day to maintain proper hydration levels.

However, this may vary depending on factors such as age, activity level, and climate. In addition to drinking water, consuming hydrating foods can further support liver health. Water-rich fruits and vegetables, such as watermelon, cucumber, and strawberries, provide hydration along with valuable nutrients and antioxidants that promote the liver's well-being.

Watermelon, for example, not only quenches thirst but also contains an amino acid called citrulline, which helps the liver remove ammonia, a waste product of protein metabolism. Cucumbers, on the other hand, are not only hydrating but also rich in antioxidants that protect liver cells from damage.

Strawberries, packed with vitamins and antioxidants, contribute to the liver's overall health and function. Other hydrating foods that support liver health include oranges, grapefruits, celery, and lettuce.

10 Natural Supplements for Healthy Liver Function e10 Leafy greens are also a great source of chlorophyll which is instrumental in clearing toxins from the body. Individual groups were compared to the Bifidobacteria dominated group, the unique cluster in this patient cohort. Anaerobe 59 , — Article CAS PubMed Google Scholar Leonhardt, J. The foods you consume are important for your liver health.
How to optimize referral timing for severe liver disease - Mayo Clinic

The second is implementing BOA's initialization with Optimization Based on Opposition OBO. Finally, five different classifiers, which are Support Vector Machine SVM , K-Nearest Neighbor KNN , Naive Bayes NB , Decision Tree DT , and Random Forest RF are used to identify patients with liver disease during the detection phase.

Results from a battery of experiments show that the proposed IB 2 OA outperforms the state-of-the-art methods in terms of precision, accuracy, recall, and F-score. In addition, when compared to the state-of-the-art, the proposed model's average selected features score is 4.

In addition, among all classifiers considered, KNN classifier achieved the highest classification accuracy on the test dataset.

Ashikur Rahman Khan, Faria Afrin, … Zayed-Us-Salehin. The liver organ plays a critical role in many functions in the human body, including red blood cell decomposition.

The liver is the largest organ in our body, and it is found in the upper right corner of our abdomen. Actually, any abnormality of the liver is referred to as liver disease. Some of the things that can happen with this disease are inflammation hepatitis B and C from infectious or non-infectious causes chemical or autoimmune hepatitis , tumors, malignant scarring of the liver cirrhosis , and metabolic disorders.

Liver diseases can be caused by a variety of factors. High cholesterol, autoimmune disorders, and long-term use of medications are among them [ 1 ].

In the last few decades, liver diseases have rapidly increased in prevalence and severity, making them one of the leading global killers. Ultrasound US , Computed Tomography CT , and Magnetic Resonance Imaging MRI are just some of the imaging modalities used to diagnose liver disease [ 3 , 4 ].

Nonetheless, it's possible that routine blood tests are essential in preventing liver disease [ 4 ]. The information age we are currently experiencing generates millions of data points daily from a wide variety of sources.

Using machine learning techniques, these data can be used to enhance healthcare services and accurately diagnose diseases. Researchers have paid a lot of focus to Machine Learning ML , and it has been widely adopted and used in a wide variety of contexts around the world. In medicine, ML has demonstrated its effectiveness by being used to address a variety of urgent issues, including cancer treatment, heart disease diagnosis, dengue fever treatment, and other issues [ 5 ].

High data dimensionality is a problem that frequently arises in ML. Consequently, a large amount of memory is required, and sometimes these data may be irrelevant or redundant, resulting in an overfitting problem.

Therefore, feature selection is carried out as a means of dealing with this issue. Feature selection is the process of deleting the less informative features and selecting the most informative ones [ 6 ]. Actually, it is too challenging to learn effective classifiers for many classification problems without first removing redundant features.

It is possible to reduce the complexity of learning algorithms by removing superfluous features. There are numerous feature selection strategies available for determining which ones are the most informative. There are two types of these procedures: filter procedures and wrapper procedures [ 7 , 8 , 9 ].

Without a learning algorithm and instead using broad characteristics of the data, filter methods evaluate and select feature subsets. In contrast, wrapper approaches use a classification algorithm to assess a feature subset after an optimizing algorithm has been applied to either add or remove features [ 7 , 8 , 9 ].

Figures 1 and 2 depict respective examples of filter and wrapper techniques. The primary contribution of this paper is the presentation of a new Liver Patients Detection Strategy LPDS that utilizes common blood tests for both Liver and non-Liver patients. In fact, the proposed LPDS is divided into three stages: i data preprocessing; ii feature selection; and iii detection.

The primary goal of the data preprocessing phase is to eliminate any outliers from the input data using Isolation Forest IF. In reality, IB 2 OA is a hybrid technique that incorporates both filter and wrapper strategies.

There are two steps to IB 2 OA; the Primary Selection PS and the Final Selection FS. In fact, PS is used to quickly identify the most important features.

While FS is used to accurately select the most informative features. During detection phase, liver disease patients are detected based on the most efficacious features.

Experimental results demonstrated that IB 2 OA outperforms other competitors as it introduces maximum accuracy. Following this outline, the rest of the paper is presented as follows: Section 2 details the guiding principles of this piece. In Section 3 , we will discuss the efforts made in the past to categorize patients with liver disease.

The Liver Patients Detection Strategy LPDS is the topic of Section 4 's extensive analysis. In Section 5 , we discuss our findings from the experiments. In Section 6 , conclusions are summarized. The principles used in this article include; swarm intelligence concept, butterfly optimization algorithm, and opposite based learning which are covered in detail in the following subsections.

Swarm Intelligence SI is a method for modelling cooperative intelligence in biological systems. Natural swarm behavior is the basis for this well-liked multi-agent framework [ 10 ]. It acts similarly to how a pack of animals would in order to stay alive. Swarm behavior is advantageous for many kinds of animals in many different environments.

According to models of basic group behavior, even relatively simple interactions between individuals can be enough to shape and display a variety of group morphologies. People form groups in order to share the burden of processing information and making business-related decisions.

The group's ability to make better decisions than an individual does is known as "collective intelligence" [ 11 , 12 ]. SI refers to the mechanism by which individuals interact with one another and the groups to which they belong [ 11 , 12 ].

SI has been applied to the management of robots and unmanned vehicles, the forecasting of social behaviors, and the enhancement of computer and communication networks [ 10 , 13 , 14 ]. The field of SI algorithms is often thought of as a subset of AI.

SI has recently attracted attention from the feature selection community [ 10 , 13 , 14 ] due to its ease of use and global search capabilities. Numerous algorithms that use swarm intelligence have been developed, such as Particle Swarm Optimization PSO , Grey Wolf Optimizer GWO , Genetic Algorithm GA , Whale Optimizer Algorithm WOA , Salp Swarm Algorithm SSA , the Sine Cosine Algorithm SCA , Bat Algorithm BA , Ant Colony Optimization ACO , and Butterfly Optimization Algorithm BOA [ 14 , 15 ].

The search for the next iteration of any SI algorithm typically relies on a stochastic search algorithm, in which heuristic information is shared. The overarching structure of SI algorithms is depicted in Fig.

It's essential to set the algorithm's parameters up front. Next, initialization and the accompanying strategies kick off the evolutionary process. In the next iteration of the SI framework, the fitness function will be used to rank the search agents.

The fitness function can be either a simple metric, such as classification accuracy, or a more complex function. In a SI algorithm, the search agents are periodically updated and relocated in accordance with the algorithm's underlying mathematics.

This procedure is carried out over and over again until the end condition is met. In the end, the optimal search result is found [ 14 , 15 ]. Butterfly Optimization Algorithm BOA is an example of a bio-inspired algorithm, a class of metaheuristic algorithms that takes inspiration from the natural world.

Butterflies are used as search agents in the optimization process of BOA, which is based on their behavior when foraging for food [ 16 , 17 ]. Butterflies are equipped with olfactory receptors that allow them to detect the aroma of food and flowers.

Chemoreceptors are the sensory receptors that are found all throughout the butterfly's body. A butterfly is thought to be able to create scent or fragrance with some power or intensity in BOA [ 16 , 17 ]. This scent has something to do with the butterfly's fitness, which is determined by the problem's objective function.

This means that a butterfly's fitness will change as it moves from one position in the search space to another. The scent of a butterfly can be detected by other butterflies in the neighborhood, resulting in the formation of a collective social learning system.

When a butterfly detects the scent of the best butterfly in the search space, it makes a beeline for it, and this stage is known as the BOA global search phase.

While a butterfly in the search space is unable to detect the scent of another butterfly, this step is known as the local search phase since it will make random strides [ 16 , 17 , 18 , 19 ].

In BOA as in any SI algorithm, there are three phases which are; initialization phase, iteration phase, and final phase [ 20 ]. In the first phase i. Then, the algorithm enters the stage of iteration phase where the search agents use two phases which are; global search phase and local search phase according to a p.

switch probability Actually, p controls the algorithm strategy for both global and local searches [ 20 ]. The global and local searches of BOA, are determined using the following equations:.

Y i t donates the position of i th butterfly in the current repetition t, r is a random number between zero and one. In addition, Y best t the location of the best butterfly in repetition t and Y j t and Y k t stand for j th and k th butterflies from the population in the current repetition t.

f represents the perceived magnitude of the fragrance which is formulated by using the following equation:. Where c is the sensory modality, I is the stimulus intensity and a is the power exponent.

In BOA equations 1 and 2 are used according to following strategy:. Figure 4 shows the flow chart of convolutional BOA and conventional BOA algorithm is represented in algorithm 1.

Opposition-Based Learning OBL is a new intelligent computing technology that has been successfully applied in many intelligent algorithm optimizations. OBL was proposed by Tizhoosh [ 21 ]. It has been theorized that OBL has a higher chance of finding a solution very close to the global optimal solution [ 22 ].

Previous efforts to categorize liver diseases were discussed in this section. An Image-based Classification Model ICM for liver disease classification has been proposed in [ 23 ] with novel methodology. The proposed method did go through several iterations.

The used CT images were first preprocessed by cutting out the background and focusing in on the liver. Intensity and higher-order features were used to derive the 3D texture features.

Then, we used a hybrid of the Whale Optimization Approach and Simulated Annealing WOA-SA to select the most effective features for further analysis. The proposed classification model is then fed with these informative features. The experimental results show that the proposed method outperforms the alternatives.

A machine learning-based Fatty Liver Disease Classification Model FLDCM is presented in [ 24 ]. Using four different classification techniques, the developed model successfully predicted fatty liver disease. Random Forest, Naive Bayes, Artificial Neural Networks, and Logistic Regression are the models in question.

In the first stage of data preparation, all gaps in the data set were closed. The relative importance of each variable was then calculated using Information Gain IG. Then, accurate FLD patient identification and predictive classification models were created.

Compared to other classification models, the RF model performed better, as shown by the results in [ 24 ]. According to [ 25 ], a novel approach has been proposed to classify patients with liver cancer by using Serum Raman Spectroscopy in conjunction with Deep Learning Algorithms SRS-DLA.

Gaussian white noise was added to the data at levels of 5, 10, 15, 20, and 25 dB to increase the robustness of the proposed models. Convolutional neural networks that were fed data that had been enhanced by a factor of ten reportedly performed well, as shown in [ 25 ].

Using gadoxetic acid enhanced Hepatobiliary Phase HBP MRI, a fully automated Deep Learning DL algorithm has been introduced [ 26 ]. In the first step, convolutional neural network CNN input was generated from HBP images by creating representative liver patch images for each patient.

Later on, the DL model was fed segmented liver images that had been patched together. As shown in [ 26 ], noninvasive liver fibrosis staging has good-to-excellent diagnostic performance in experimental settings.

Computer-assisted diagnosis CAD using ultrasound images of the liver has been developed, as detailed in [ 27 ]. The proposed CAD method used a voting-based classifier and machine learning algorithms to determine whether liver tissues were fatty or normal.

First, a genetic algorithm was used to select multiple regions of interest ROIs; a total of nine ROIs within the liver tissue. Then, using Gray-Level Co-Occurrence Matrix GLCM and First-Order Statistics FOS , 26 features of each ROI were extracted.

Finally, fatty liver tissue was classified using a voting-based classifier. The results obtained in [ 27 ] show that the proposed CAD method outperformed the existing literature. In [ 28 ], a novel framework for early detection of chronic liver disease is proposed. A model known as a Hybridized Rough Set and Bat-inspired Algorithm HRS-BA is being proposed.

The primary goal of the proposed model is to give the doctor a new perspective. Decision-making factors were initially prioritized using BA.

The decision rules were then created based on these characteristics. The results were also compared to those obtained by using hybridized decision tree algorithms, and they were found to be vastly superior. Current classification methods are briefly compared in Table 1. This section will go over the proposed Liver Patients Detection Strategy LPDS.

LPDS's primary goal is to detect patients who are infected with liver disease quickly and accurately. The early detection of liver disease patients allows for faster treatment and, consequently, slows the disease's spread. LPDS receives input in the form of a training set consisting of both normal and patient routine blood tests.

After the model has been trained, it will be able to classify new cases. LPDS determines whether or not the input case is infected. As shown in Fig. The details of each phase will be discussed in the next subsection.

The primary purpose of the preprocessing stage is to ready the data for the subsequent processing stage. Firstly, patient attributes are first extracted from the input training set.

Table 2 lists several characteristics to consider when detecting liver disease patients. The dataset contains four missing values. However, special care must be taken with outliers.

Two methods are employed. The results were unfavourable when machine learning algorithms were applied directly to the data without first removing outliers or selecting features. Results using the dataset's normal distribution to combat overfitting and then applying Isolation Forest IS for outlier detection are, however, quite encouraging [ 29 , 30 ].

Several methods of plotting were used to examine the data for skewness, detect outliers, and verify the data's distribution. To succeed, each of these preprocessing methods is essential. The most instructive features are chosen during the feature selection phase. The term "feature selection" describes this procedure.

Feature selection is a method for increasing the classifier's accuracy by eliminating extraneous data points.

Therefore, the feature selection process is crucial to enhancing the efficacy of learning algorithms [ 25 , 31 , 32 ]. Filter and wrapper methods are the two most common ways to categorize feature selection techniques [ 31 , 32 ]. It has been demonstrated that filter methods are fast and scalable but cannot provide better performance than wrapper methods.

However, wrapper methods are more expensive to compute [ 31 , 32 , 33 ], despite providing better performance. The Improved Binary Butterfly Optimization Algorithm IB 2 OA is proposed in this work as a new hybrid filter-wrapper approach to feature selection.

By combining the speed of the filter technique with the strength of the wrapper method, IB 2 OA is able to select features from the dataset with greater efficiency. For this reason, the proposed method seeks to simplify the calculations required to locate the optimal solution to high-dimensional datasets and cut down on the time spent doing so.

Figure 6 shows that the core of the Improved Binary Butterfly Optimization Algorithm IB 2 OA is made up of two parts: i the Primary Selection PS using Information Gain IG , and ii the Final Selection FS using IB 2 OA.

The large search space slows down IB 2 OA's computation time, despite its ability to accurately identify the effective features. Therefore, the primary goal of PS is to apply IG to select the most effective features by narrowing the search space of B 2 OA, thereby reducing the time complexity.

When all is said and done, the optimal subset of features helps enhance the reliability and performance of the employed classification model. Figure 6 shows how IG is used in PS to select the best possible set of useful and informative features to use when analyzing data from a dataset of liver patients.

Figure 6 depicts the extraction of features from a dataset of liver patients, followed by their transfer to the first step e. Therefore, the PS output will be fed into the second step e.

Then, IB 2 OA will be performed until the end point is reached. In the end, the most effective set of features is provided by the best solution for the population, IB 2 OA.

Base classifiers, such as Naive Bayes NB , should be used to assess this subset [ 34 , 35 , 36 ]. In general, IB 2 OA is relied on a meta-heuristic optimization algorithm called Butterfly Optimization Algorithm BOA.

BOA simulates the guidance and hunting behavior of the butterflies in search of food in natural environment. It was used to solve continuous optimization problem.

Hence, to deal with feature selection problem which is considered discrete optimization problem, BOA is converted into B 2 OA. Hence, B 2 OA starts with a group of butterflies as solutions which are called Population P. Each butterfly represents a candidate solution e.

For features, a value of 'one' indicates selection, while a value of 'zero' indicates deselection or removal. Figure 6 shows the required sequential steps for implementing IB 2 AO as a feature selection. In other word, IG is used to rank the features based on its entropy using the following equation [ 37 ]:.

Where C n is the n th class category, f is stand for the feature. P C n denotes the percentage of reviews in the C n class category, and P f is the percentage of reviews in the f class category. Then, this subset of optimal features will be passed to the second step e.

Then, the whole solutions will be evaluated using the accuracy index of a standard classifier such as NB to find the best subset of features. for example, if the number of initial solutions is 20 solutions, then, the number of opposite solutions is also 20 solutions. Consequently, the final population contains 40 solutions and these solutions will be evaluated using the following equation:.

Where Accuracy Y i is the success rate of classifying data using the i th set of features. The algorithm searches for the best butterfly with the highest Fit Y i. After evaluating all candidate solution, the optimization procedure can be used to change the position of a butterfly that has been placed artificially using equation 4.

After updating the new positions of the butterflies, these positions are position is adjusted using the sigmoid function, which is used to find new butterfly position relied on binary values by using 9 :. Then, the process is repeated up to the maximum allowed generations. Once the best butterfly has been selected from the population, the algorithm stops.

All features contributed by 1 in this butterfly are the most reliable indicators of liver disease. Different features will be chosen as the best subset of features after the IB 2 OA algorithm is applied to the dataset of liver patients.

Algorithm 2 depicts the proposed IB2OA's algorithm. Finally, in the detection phase, different ML classifiers are used. Actually, the development in computer vision and ML technologies can be used for the accurate, quick, and earlier detection of liver disease patients [ 38 ]. Utilizing these technologies has the advantage of producing quick and precise results from computerized arrests.

Time wastage can be decreased by utilizing improvements in computer vision and precision. Patients with liver disease benefit from being diagnosed early so that they can begin treatment as soon as possible.

In this paper, the selected features are used to fed five different classifiers which are; Support Vector Machine SVM , K- Nearest Neighbor KNN , Naïve Bayes NB , Decision Tree DT , and Random Forest RF as shown in Fig.

Based on classification accuracy, the effectiveness of various classifiers was evaluated. The effectiveness of the Liver Patients Detection Strategy LPDS that was just proposed will be discussed here. The proposed LPDS was used to identify infected patients with liver disease from laboratory results.

In reality, there are three stages to LPDS: i data preprocessing, ii feature selection, and iii detection. During data preprocessing, the patient's information is managed, and anomalous data is eliminated. Following this, the most useful features are selected utilizing Improved Binary Butterfly Optimization Algorithm IB 2 OA during the feature selection phase.

At last, these useful features are fed into five distinct classifiers: SVM, KNN, NB, DT, and RF. The most efficient of these classifiers will be selected on the basis of their ability to correctly categorize data. With the help of the collected data patients' dataset , the results presented in this paper were generated.

Given the scarcity of publicly available datasets, the classification model is verified via cross-validation. Using fold cross-validation, the dataset is split into 10 equal parts, with one part serving as the testing set and the other 9 as the training sets.

The information age we are currently experiencing generates millions of data points daily from a wide variety of sources. Using machine learning techniques, these data can be used to enhance healthcare services and accurately diagnose diseases. Researchers have paid a lot of focus to Machine Learning ML , and it has been widely adopted and used in a wide variety of contexts around the world.

In medicine, ML has demonstrated its effectiveness by being used to address a variety of urgent issues, including cancer treatment, heart disease diagnosis, dengue fever treatment, and other issues [ 5 ].

High data dimensionality is a problem that frequently arises in ML. Consequently, a large amount of memory is required, and sometimes these data may be irrelevant or redundant, resulting in an overfitting problem.

Therefore, feature selection is carried out as a means of dealing with this issue. Feature selection is the process of deleting the less informative features and selecting the most informative ones [ 6 ]. Actually, it is too challenging to learn effective classifiers for many classification problems without first removing redundant features.

It is possible to reduce the complexity of learning algorithms by removing superfluous features. There are numerous feature selection strategies available for determining which ones are the most informative.

There are two types of these procedures: filter procedures and wrapper procedures [ 7 , 8 , 9 ]. Without a learning algorithm and instead using broad characteristics of the data, filter methods evaluate and select feature subsets.

In contrast, wrapper approaches use a classification algorithm to assess a feature subset after an optimizing algorithm has been applied to either add or remove features [ 7 , 8 , 9 ]. Figures 1 and 2 depict respective examples of filter and wrapper techniques.

The primary contribution of this paper is the presentation of a new Liver Patients Detection Strategy LPDS that utilizes common blood tests for both Liver and non-Liver patients. In fact, the proposed LPDS is divided into three stages: i data preprocessing; ii feature selection; and iii detection.

The primary goal of the data preprocessing phase is to eliminate any outliers from the input data using Isolation Forest IF. In reality, IB 2 OA is a hybrid technique that incorporates both filter and wrapper strategies.

There are two steps to IB 2 OA; the Primary Selection PS and the Final Selection FS. In fact, PS is used to quickly identify the most important features. While FS is used to accurately select the most informative features. During detection phase, liver disease patients are detected based on the most efficacious features.

Experimental results demonstrated that IB 2 OA outperforms other competitors as it introduces maximum accuracy. Following this outline, the rest of the paper is presented as follows: Section 2 details the guiding principles of this piece.

In Section 3 , we will discuss the efforts made in the past to categorize patients with liver disease. The Liver Patients Detection Strategy LPDS is the topic of Section 4 's extensive analysis. In Section 5 , we discuss our findings from the experiments. In Section 6 , conclusions are summarized.

The principles used in this article include; swarm intelligence concept, butterfly optimization algorithm, and opposite based learning which are covered in detail in the following subsections.

Swarm Intelligence SI is a method for modelling cooperative intelligence in biological systems. Natural swarm behavior is the basis for this well-liked multi-agent framework [ 10 ]. It acts similarly to how a pack of animals would in order to stay alive.

Swarm behavior is advantageous for many kinds of animals in many different environments. According to models of basic group behavior, even relatively simple interactions between individuals can be enough to shape and display a variety of group morphologies.

People form groups in order to share the burden of processing information and making business-related decisions. The group's ability to make better decisions than an individual does is known as "collective intelligence" [ 11 , 12 ].

SI refers to the mechanism by which individuals interact with one another and the groups to which they belong [ 11 , 12 ]. SI has been applied to the management of robots and unmanned vehicles, the forecasting of social behaviors, and the enhancement of computer and communication networks [ 10 , 13 , 14 ].

The field of SI algorithms is often thought of as a subset of AI. SI has recently attracted attention from the feature selection community [ 10 , 13 , 14 ] due to its ease of use and global search capabilities.

Numerous algorithms that use swarm intelligence have been developed, such as Particle Swarm Optimization PSO , Grey Wolf Optimizer GWO , Genetic Algorithm GA , Whale Optimizer Algorithm WOA , Salp Swarm Algorithm SSA , the Sine Cosine Algorithm SCA , Bat Algorithm BA , Ant Colony Optimization ACO , and Butterfly Optimization Algorithm BOA [ 14 , 15 ].

The search for the next iteration of any SI algorithm typically relies on a stochastic search algorithm, in which heuristic information is shared. The overarching structure of SI algorithms is depicted in Fig.

It's essential to set the algorithm's parameters up front. Next, initialization and the accompanying strategies kick off the evolutionary process. In the next iteration of the SI framework, the fitness function will be used to rank the search agents.

The fitness function can be either a simple metric, such as classification accuracy, or a more complex function. In a SI algorithm, the search agents are periodically updated and relocated in accordance with the algorithm's underlying mathematics. This procedure is carried out over and over again until the end condition is met.

In the end, the optimal search result is found [ 14 , 15 ]. Butterfly Optimization Algorithm BOA is an example of a bio-inspired algorithm, a class of metaheuristic algorithms that takes inspiration from the natural world.

Butterflies are used as search agents in the optimization process of BOA, which is based on their behavior when foraging for food [ 16 , 17 ].

Butterflies are equipped with olfactory receptors that allow them to detect the aroma of food and flowers. Chemoreceptors are the sensory receptors that are found all throughout the butterfly's body. A butterfly is thought to be able to create scent or fragrance with some power or intensity in BOA [ 16 , 17 ].

This scent has something to do with the butterfly's fitness, which is determined by the problem's objective function. This means that a butterfly's fitness will change as it moves from one position in the search space to another.

The scent of a butterfly can be detected by other butterflies in the neighborhood, resulting in the formation of a collective social learning system. When a butterfly detects the scent of the best butterfly in the search space, it makes a beeline for it, and this stage is known as the BOA global search phase.

While a butterfly in the search space is unable to detect the scent of another butterfly, this step is known as the local search phase since it will make random strides [ 16 , 17 , 18 , 19 ].

In BOA as in any SI algorithm, there are three phases which are; initialization phase, iteration phase, and final phase [ 20 ]. In the first phase i. Then, the algorithm enters the stage of iteration phase where the search agents use two phases which are; global search phase and local search phase according to a p.

switch probability Actually, p controls the algorithm strategy for both global and local searches [ 20 ]. The global and local searches of BOA, are determined using the following equations:.

Y i t donates the position of i th butterfly in the current repetition t, r is a random number between zero and one. In addition, Y best t the location of the best butterfly in repetition t and Y j t and Y k t stand for j th and k th butterflies from the population in the current repetition t.

f represents the perceived magnitude of the fragrance which is formulated by using the following equation:. Where c is the sensory modality, I is the stimulus intensity and a is the power exponent. In BOA equations 1 and 2 are used according to following strategy:.

Figure 4 shows the flow chart of convolutional BOA and conventional BOA algorithm is represented in algorithm 1. Opposition-Based Learning OBL is a new intelligent computing technology that has been successfully applied in many intelligent algorithm optimizations.

OBL was proposed by Tizhoosh [ 21 ]. It has been theorized that OBL has a higher chance of finding a solution very close to the global optimal solution [ 22 ]. Previous efforts to categorize liver diseases were discussed in this section.

An Image-based Classification Model ICM for liver disease classification has been proposed in [ 23 ] with novel methodology. The proposed method did go through several iterations.

The used CT images were first preprocessed by cutting out the background and focusing in on the liver. Intensity and higher-order features were used to derive the 3D texture features. Then, we used a hybrid of the Whale Optimization Approach and Simulated Annealing WOA-SA to select the most effective features for further analysis.

The proposed classification model is then fed with these informative features. The experimental results show that the proposed method outperforms the alternatives. A machine learning-based Fatty Liver Disease Classification Model FLDCM is presented in [ 24 ].

Using four different classification techniques, the developed model successfully predicted fatty liver disease. Random Forest, Naive Bayes, Artificial Neural Networks, and Logistic Regression are the models in question.

In the first stage of data preparation, all gaps in the data set were closed. The relative importance of each variable was then calculated using Information Gain IG.

Then, accurate FLD patient identification and predictive classification models were created. Compared to other classification models, the RF model performed better, as shown by the results in [ 24 ].

According to [ 25 ], a novel approach has been proposed to classify patients with liver cancer by using Serum Raman Spectroscopy in conjunction with Deep Learning Algorithms SRS-DLA. Gaussian white noise was added to the data at levels of 5, 10, 15, 20, and 25 dB to increase the robustness of the proposed models.

Convolutional neural networks that were fed data that had been enhanced by a factor of ten reportedly performed well, as shown in [ 25 ].

Using gadoxetic acid enhanced Hepatobiliary Phase HBP MRI, a fully automated Deep Learning DL algorithm has been introduced [ 26 ]. In the first step, convolutional neural network CNN input was generated from HBP images by creating representative liver patch images for each patient.

Later on, the DL model was fed segmented liver images that had been patched together. As shown in [ 26 ], noninvasive liver fibrosis staging has good-to-excellent diagnostic performance in experimental settings. Computer-assisted diagnosis CAD using ultrasound images of the liver has been developed, as detailed in [ 27 ].

The proposed CAD method used a voting-based classifier and machine learning algorithms to determine whether liver tissues were fatty or normal. First, a genetic algorithm was used to select multiple regions of interest ROIs; a total of nine ROIs within the liver tissue. Then, using Gray-Level Co-Occurrence Matrix GLCM and First-Order Statistics FOS , 26 features of each ROI were extracted.

Finally, fatty liver tissue was classified using a voting-based classifier. The results obtained in [ 27 ] show that the proposed CAD method outperformed the existing literature. In [ 28 ], a novel framework for early detection of chronic liver disease is proposed. A model known as a Hybridized Rough Set and Bat-inspired Algorithm HRS-BA is being proposed.

The primary goal of the proposed model is to give the doctor a new perspective. Decision-making factors were initially prioritized using BA. The decision rules were then created based on these characteristics. The results were also compared to those obtained by using hybridized decision tree algorithms, and they were found to be vastly superior.

Current classification methods are briefly compared in Table 1. This section will go over the proposed Liver Patients Detection Strategy LPDS. LPDS's primary goal is to detect patients who are infected with liver disease quickly and accurately. The early detection of liver disease patients allows for faster treatment and, consequently, slows the disease's spread.

LPDS receives input in the form of a training set consisting of both normal and patient routine blood tests. After the model has been trained, it will be able to classify new cases. LPDS determines whether or not the input case is infected.

As shown in Fig. The details of each phase will be discussed in the next subsection. The primary purpose of the preprocessing stage is to ready the data for the subsequent processing stage. Firstly, patient attributes are first extracted from the input training set.

Table 2 lists several characteristics to consider when detecting liver disease patients. The dataset contains four missing values.

However, special care must be taken with outliers. Two methods are employed. The results were unfavourable when machine learning algorithms were applied directly to the data without first removing outliers or selecting features.

Results using the dataset's normal distribution to combat overfitting and then applying Isolation Forest IS for outlier detection are, however, quite encouraging [ 29 , 30 ]. Several methods of plotting were used to examine the data for skewness, detect outliers, and verify the data's distribution.

To succeed, each of these preprocessing methods is essential. The most instructive features are chosen during the feature selection phase. The term "feature selection" describes this procedure. Feature selection is a method for increasing the classifier's accuracy by eliminating extraneous data points.

Therefore, the feature selection process is crucial to enhancing the efficacy of learning algorithms [ 25 , 31 , 32 ]. Filter and wrapper methods are the two most common ways to categorize feature selection techniques [ 31 , 32 ].

It has been demonstrated that filter methods are fast and scalable but cannot provide better performance than wrapper methods. However, wrapper methods are more expensive to compute [ 31 , 32 , 33 ], despite providing better performance. The Improved Binary Butterfly Optimization Algorithm IB 2 OA is proposed in this work as a new hybrid filter-wrapper approach to feature selection.

By combining the speed of the filter technique with the strength of the wrapper method, IB 2 OA is able to select features from the dataset with greater efficiency.

For this reason, the proposed method seeks to simplify the calculations required to locate the optimal solution to high-dimensional datasets and cut down on the time spent doing so.

Figure 6 shows that the core of the Improved Binary Butterfly Optimization Algorithm IB 2 OA is made up of two parts: i the Primary Selection PS using Information Gain IG , and ii the Final Selection FS using IB 2 OA. The large search space slows down IB 2 OA's computation time, despite its ability to accurately identify the effective features.

Therefore, the primary goal of PS is to apply IG to select the most effective features by narrowing the search space of B 2 OA, thereby reducing the time complexity. When all is said and done, the optimal subset of features helps enhance the reliability and performance of the employed classification model.

Figure 6 shows how IG is used in PS to select the best possible set of useful and informative features to use when analyzing data from a dataset of liver patients. Figure 6 depicts the extraction of features from a dataset of liver patients, followed by their transfer to the first step e.

Therefore, the PS output will be fed into the second step e. Then, IB 2 OA will be performed until the end point is reached. In the end, the most effective set of features is provided by the best solution for the population, IB 2 OA.

Base classifiers, such as Naive Bayes NB , should be used to assess this subset [ 34 , 35 , 36 ]. In general, IB 2 OA is relied on a meta-heuristic optimization algorithm called Butterfly Optimization Algorithm BOA.

BOA simulates the guidance and hunting behavior of the butterflies in search of food in natural environment. It was used to solve continuous optimization problem. Hence, to deal with feature selection problem which is considered discrete optimization problem, BOA is converted into B 2 OA.

Hence, B 2 OA starts with a group of butterflies as solutions which are called Population P. Each butterfly represents a candidate solution e. For features, a value of 'one' indicates selection, while a value of 'zero' indicates deselection or removal.

Figure 6 shows the required sequential steps for implementing IB 2 AO as a feature selection. In other word, IG is used to rank the features based on its entropy using the following equation [ 37 ]:.

Where C n is the n th class category, f is stand for the feature. P C n denotes the percentage of reviews in the C n class category, and P f is the percentage of reviews in the f class category. Then, this subset of optimal features will be passed to the second step e.

Then, the whole solutions will be evaluated using the accuracy index of a standard classifier such as NB to find the best subset of features. for example, if the number of initial solutions is 20 solutions, then, the number of opposite solutions is also 20 solutions.

Consequently, the final population contains 40 solutions and these solutions will be evaluated using the following equation:. Where Accuracy Y i is the success rate of classifying data using the i th set of features.

The algorithm searches for the best butterfly with the highest Fit Y i. After evaluating all candidate solution, the optimization procedure can be used to change the position of a butterfly that has been placed artificially using equation 4.

After updating the new positions of the butterflies, these positions are position is adjusted using the sigmoid function, which is used to find new butterfly position relied on binary values by using 9 :.

Then, the process is repeated up to the maximum allowed generations. Once the best butterfly has been selected from the population, the algorithm stops.

All features contributed by 1 in this butterfly are the most reliable indicators of liver disease. Different features will be chosen as the best subset of features after the IB 2 OA algorithm is applied to the dataset of liver patients.

Algorithm 2 depicts the proposed IB2OA's algorithm. Finally, in the detection phase, different ML classifiers are used. Actually, the development in computer vision and ML technologies can be used for the accurate, quick, and earlier detection of liver disease patients [ 38 ].

Utilizing these technologies has the advantage of producing quick and precise results from computerized arrests. Time wastage can be decreased by utilizing improvements in computer vision and precision. Patients with liver disease benefit from being diagnosed early so that they can begin treatment as soon as possible.

In this paper, the selected features are used to fed five different classifiers which are; Support Vector Machine SVM , K- Nearest Neighbor KNN , Naïve Bayes NB , Decision Tree DT , and Random Forest RF as shown in Fig.

Based on classification accuracy, the effectiveness of various classifiers was evaluated. The effectiveness of the Liver Patients Detection Strategy LPDS that was just proposed will be discussed here.

The proposed LPDS was used to identify infected patients with liver disease from laboratory results. In reality, there are three stages to LPDS: i data preprocessing, ii feature selection, and iii detection.

During data preprocessing, the patient's information is managed, and anomalous data is eliminated. Following this, the most useful features are selected utilizing Improved Binary Butterfly Optimization Algorithm IB 2 OA during the feature selection phase.

At last, these useful features are fed into five distinct classifiers: SVM, KNN, NB, DT, and RF. The most efficient of these classifiers will be selected on the basis of their ability to correctly categorize data.

With the help of the collected data patients' dataset , the results presented in this paper were generated. Given the scarcity of publicly available datasets, the classification model is verified via cross-validation.

Using fold cross-validation, the dataset is split into 10 equal parts, with one part serving as the testing set and the other 9 as the training sets. Our tests were run on an Intel Core iU processor at 2. Parameters and their corresponding values are shown in Table 3. Information collected from patients' medical records is included in this dataset.

There are a total of cases in the dataset. In fact, as shown in Table 4 , the cases in the collected dataset are split into two groups: those with liver disease and those without. Hepatic patients are commonly referred to by that term. Patients who do not have liver disease are referred to as "non-liver patients.

A breakdown of the collected data set by "Age," "Gender," and "Type of Disease" is shown in Figs. The primary findings from the system used to assess the effectiveness of each algorithm are detailed in Tables 5 and 6. The confusion matrix is summarized using several different formulas in this paper, which are shown in Table 6 [ 34 , 35 , 36 ].

In this paper, a new feature selection methodology called Improved Binary Butterfly Optimization Algorithm IB 2 OA was introduced. Numerous feature selection techniques are compared to the proposed IB 2 OA based on the NB classifier as a base classifier in order to demonstrate the effectiveness of the proposed method.

They include; Particle Swarm Optimization PSO , Grey Wolf Optimizer GWO , Genetic Algorithm GA , Whale Optimizer Algorithm WOA , Salp Swarm Algorithm SSA , Sine Cosine Algorithm SCA , Bat Algorithm BA , Ant Colony Optimization ACO , and Butterfly Optimization Algorithm BOA.

Table 7 displays the results. The proposed IB 2 OA achieved the highest accuracy of 0. Furthermore, IB 2 OA introduces the highest precision value of 0. IB 2 OA's average recall and F-score are, respectively, 0. Table 7 reveals that IB 2 OA performs significantly better than PSO, GWO, GA, WOA, SSA, SCA, BA, ACO, and BOA.

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Liver Function Optimization

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