Chronic Liver Disease Classification Using Hybrid Whale Optimization with Simulated Annealing and Ensemble Classifier

Rajathi, G. Ignisha; Jiji, G. Wiselin

doi:10.3390/sym11010033

Cited by 19 publications

(7 citation statements)

References 58 publications

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“…The final quantitative results of the proposed algorithm proved the performance of the proposed algorithm [ 140 ]. In [ 141 ], the researchers used an algorithm to detect chronic liver disease, leading to total liver dysfunction. A total of 73 features were extracted from all datasets, including images of normal liver, fatter liver, cancerous liver, etc.…”

Section: Related Workmentioning

confidence: 99%

A Systematic Review on Metaheuristic Optimization Techniques for Feature Selections in Disease Diagnosis: Open Issues and Challenges

Kaur¹,

Kumar

Koul

et al. 2022

Arch Computat Methods Eng

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There is a need for some techniques to solve various problems in today’s computing world. Metaheuristic algorithms are one of the techniques which are capable of providing practical solutions to such issues. Due to their efficiency, metaheuristic algorithms are now used in healthcare data to diagnose diseases practically and with better results than traditional methods. In this study, an efficient search has been performed where 173 papers from different research databases such as Scopus, Web of Science, PubMed, PsycINFO, and others have been considered impactful in diagnosing the diseases using metaheuristic techniques. Ten metaheuristic techniques have been studied, which include spider monkey, shuffled frog leaping algorithm, cuckoo search algorithm, ant lion technique of optimization, lion optimization technique, moth flame technique, bat-inspired algorithm, grey wolf algorithm, whale optimization, and dragonfly technique of optimization for selecting and optimizing the features to predict heart disease, Alzheimer's disease, brain disorder, diabetes, chronic disease features, liver disease, covid-19, etc. Besides, the framework has also been shown to provide information on various phases behind the execution of metaheuristic techniques to predict diseases. The study’s primary goal is to present the contribution of the researchers by demonstrating their methodology to predict diseases using the metaheuristic techniques mentioned above. Later, their work has also been compared and evaluated using accuracy, precision, F1 score, error rate, sensitivity, specificity, an area under a curve, etc., to help the researchers to choose the right field and methods for predicting the diseases in the future.

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Section: Related Workmentioning

confidence: 99%

A Systematic Review on Metaheuristic Optimization Techniques for Feature Selections in Disease Diagnosis: Open Issues and Challenges

Kaur¹,

Kumar

Koul

et al. 2022

Arch Computat Methods Eng

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“…The result showed a 96% of accuracy rate. Rajathi et al 17 proposed a chronic liver disease (CLD) classification using hybrid whale optimization with simulated annealing and ensemble classifier. The noninvasive approach was used for examining the CLD and the support vector machine was used to classify the liver disease.…”

Section: Related Workmentioning

confidence: 99%

A novel modified long short term memory architecture for automatic liver disease prediction from patient records

Daniel¹,

Ramaraj²

2022

Concurrency and Computation

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The liver is the second largest organ in the human body after the skin and liver disease mainly impacts the liver's functionality by properly separating the nutrients and waste into the digestive system and also causes scarring (cirrhosis) as time passes. The scarring over time affects the healthy liver tissue and also affects its proper functioning and if left untreated for a prolonged period it can also result in severe complications such as liver failure or liver cancer. The patients can be prevented from the severe complications if the disease is detected at an earlier stage and the existing research for liver disease prediction mainly encouraged the usage of intelligent machine learning-based techniques. However, these techniques have several complexities such as low accuracy, overfitting, higher training time, poor feature extraction capabilities and so on.To overcome these problems, we present modified long short term emory (MLSTM) architecture for chronic liver disease prediction. The proposed methodology has three stages: information enhancement, feature extraction, and classification. The modified generative adversarial network uses an autoencoder system for sample augmentation which helps to enrich the diversity present in both the normal and abnormal classes.The outlier information is eliminated via the criminal search algorithm which captures the differences and correlation associated with multiple samples. The fast independent component analysis algorithm and enhanced whale optimization algorithm are used for feature extraction. This step mainly identifies the crucial features for liver disease prediction and leaves out the irrelevant and duplicate features thus enhancing the convergence, computational time, and prediction accuracy. The MLSTM architecture is used to classify the samples present in the liver disease datasets into normal and abnormal (liver disease) classes. The proposed methodology offers improved performance in terms of accuracy, recall, means square error, and F-measure. The results show that the proposed methodology will be efficient for doctors to diagnose liver disease in the earlier stage.

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“…So, those features will be mined using data mining and soft computing techniques. A noninvasive approach for evaluating CLD with computed tomography (CT) images was proposed by Rajathi et al, 22 using an ensemble of classifiers. To accurately classify CLD, the hybrid whale optimization algorithm with simulated annealing (WOA-SA) is used in selecting an optimal set of features.…”

Section: Literature Reviewmentioning

confidence: 99%

Early detection of liver disorders using hybrid soft computing techniques for optimal feature selection and classification

Varchagall

Yogegowda

2022

Concurrency and Computation

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Summary Human liver disease is a sort of illness that starts in the ovaries and is particularly dangerous for women. As a consequence, aberrant cells develop that have the potential to spread to other parts of the body. Liver disease is a serious disorder that affects women's ovaries and is difficult to identify early on, which is why it is still one of the leading causes of mortality. The significance of unequivocal confirmation of intrinsic and typical components in establishing new frameworks to detect and eliminate danger is substantial. In this study, we present a hybrid soft computing approach for early diagnosis of liver problems (EDLD‐HS). For high and low level feature extraction models, we first employ an improved ant swarm optimization (IASO) approach. Then, for optimum feature selection, we develop a modified whale search optimization (MWSO) technique that combines features that may reflect both texture patterns and semantic backdrop scattered in the data. After that, we employed a hybrid swallow swarm intelligent‐deep neural network (HSSI‐DNN) classifier to determine the stage of liver illness. Finally, we used MATLAB R2014a to test our proposed EDLD‐HS method using well‐known benchmark datasets including BUPA, ILPD, and MPRLPD. The simulation findings are compared to existing state‐of‐the‐art methodologies in terms of accuracy, specificity, sensitivity, precision, recall, F1‐score, G‐mean, and area under curve (AUC). The detection accuracy of the proposed HSSI‐DNN classifier is 83.26% (BUPA), 84.26% (ILPD), and 91.23% (ILPD) (MPRLPD).

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Chronic Liver Disease Classification Using Hybrid Whale Optimization with Simulated Annealing and Ensemble Classifier

Cited by 19 publications

References 58 publications

A Systematic Review on Metaheuristic Optimization Techniques for Feature Selections in Disease Diagnosis: Open Issues and Challenges

A Systematic Review on Metaheuristic Optimization Techniques for Feature Selections in Disease Diagnosis: Open Issues and Challenges

A novel modified long short term memory architecture for automatic liver disease prediction from patient records

Early detection of liver disorders using hybrid soft computing techniques for optimal feature selection and classification

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