[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122725-en":3,"doc-seo-122725-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122725,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",7,"Healthcare","An Ensembling Approach to Predict Hepatitis in Patients with Liver Disease Using Machine Learning","Liver disease, including hepatitis C, causes substantial mortality and often requires early detection to prevent progression to cirrhosis where options are limited to liver transplantation. The work builds a predictive pipeline using pre-processing and feature extraction on Kaggle data, then applies multiple machine learning models for classification. Logistic regression, random forest, K-nearest neighbors, and related methods are evaluated with precision, recall, F1 score, and accuracy, before combining models via ensembling. The ensemble achieves 78.96% accuracy alongside improved precision, recall, and F1 performance for earlier diagnosis.","Keywords: Liver Disease, Hepatitis, Machine learning, Ensembling, Logistica Regression, Random Forest, K-Nearest Neighbor, Support Vector Machine,  \nK-Means, Precision, Recall, F1 Score, Accuracy.  \nJournal Info:  \nSubmitted: September 13, 2023 Accepted: September 16, 2022 Published: September 20, 2022  \nAn ensembling approach to predict hepatitis in patients with liver disease using machine learning  \nMuhammad Arif1 , Mohsin Abbas 2* , Muhammad Ahmed Shehzad 2 , Zakia Batool3 Mahwish Rabia 4 , Abdul Majid Soomro 5  \n1 Department of Computer Science, KAIMS International Institute, Multan, Pakistan.; 2 Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan.; 3 Department of Statistics, Quaid-i-Azam University, Islamabad, Pakistan.; 4 Department of Statistics, Government College Women University, Sialkot, Pakistan.; 5 Department of Computer Science, NCBA&E, Multan, Pakistan.  \nAbstract  \nWith a 3.5% mortality rate, liver disease is one of the worst diseases in existence. The world is targeting this major health issue from several perspectives, to improve prevention, diagnosis, and treatment due to having the highest incidence of liver disorders. For liver problem disease, also known as HEP C is now the most prevalent disease in the world. This is due to the rapid progression of HEP C, which can only be stopped by early diagnosis. If not, it progresses to the last stage of HEP C cirrhosis, which has no other treatment options besides liver transplantation. One and only machine learning algorithms like LR, RF, KNN, XGBoost and K-Means can be used to predict liver illness utilizing modern methods like artificial intelligence. Data is gathered from Kaggle and subjected to several machine learning algorithms after pre processing in order to quickly diagnose liver disease. In this work, liver disease is predicted early on using pre-processing, feature extraction, and classification techniques. Recall, precision, and F1 score metrics are used to compare the accuracy of the six algorithms, and these algorithms are then combined to provide the most accurate diagnosis of liver disease. Additionally, to improve accuracy, all of these algorithms are ensemble, and accuracy was 78.96%, along with precision, recall, and F1 score.  \n*Correspondence author email address: [abbasmohsin202@gmail.com](abbasmohsin202@gmail.com)  \nDOI: 10.21015/vtse.v11i3 .1598  \n1 Introduction  \nOver 70 million individuals worldwide are infected with hepatitis C (HEP C), which is contagious and kills  \n0.4 million people per year. Electronic health records (EHRs) of patients can be used by doctors to more fully comprehend this condition and its prognosis be-  \nThis work is licensed under a Creative Commons Attribution 3.0 License.  \nVFAST Transactions on Software Engineering Volume 11, Issue 3, 2023  \ncause they contain information that computer-based approaches based on statistics and computational intelligence can process to reveal novel discoveries and trends that would otherwise go unnoticed by medical professionals.  \nChronic liver disease is the leading cause of death worldwide and significantly negatively affects the vast majority of people. This condition is brought on by a number of variables that have an impact on the liver. It is vital to evaluate the effectiveness of various machine learning algorithms in order to lower the high cost of predicting the presence of chronic liver disease. The performance of different classifying algorithms was evaluated using a variety of measurement methodologies, such as accuracy, precision, recall, F1 score, and specificity. Accuracy was 75%, 74%, 69%, 64%, 62%, and 53%, respectively, for logistic regression (LR), random forest (RF), decision tree (DT), support vector machine (SVM), K-Nearest Neighbors (KNN), and Naive Bayes (NB) . [1] showed that the LR has the greatest degree of accuracy.  \nEarly diagnosis is essential for the treatment and management of liver disease. Particularly in the realm of medicin","cbCailqNuuZV5zh3","https://ap.wps.com/l/cbCailqNuuZV5zh3","pdf",373855,1,11,"English","en",105,"# Introduction\n## Problem background and motivation\n## Related work and diagnostic challenges\n## Computer-aided diagnosis and role of machine learning","[{\"question\":\"Why is early diagnosis of liver disease important in this study?\",\"answer\":\"Early diagnosis enables timely treatment and management and helps prevent progression of hepatitis C toward cirrhosis, where treatment options become limited.\"},{\"question\":\"Which machine learning models are evaluated for predicting hepatitis in liver disease patients?\",\"answer\":\"The study evaluates classifiers including logistic regression and random forest, as well as other algorithms such as K-nearest neighbors, comparing their diagnostic performance.\"},{\"question\":\"How does the ensembling strategy improve prediction quality?\",\"answer\":\"Models are combined through ensembling after preprocessing and feature extraction, and the combined approach yields an accuracy of 78.96% with corresponding gains in precision, recall, and F1 score.\"}]","An Ensembling Approach to Predict Hepatitis in Patients with Liver Disease Using Machine Learning | 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is early diagnosis of liver disease important in this study?","Question",{"text":75,"@type":76},"Early diagnosis enables timely treatment and management and helps prevent progression of hepatitis C toward cirrhosis, where treatment options become limited.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are evaluated for predicting hepatitis in liver disease patients?",{"text":80,"@type":76},"The study evaluates classifiers including logistic regression and random forest, as well as other algorithms such as K-nearest neighbors, comparing their diagnostic performance.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the ensembling strategy improve prediction quality?",{"text":84,"@type":76},"Models are combined through ensembling after preprocessing and feature extraction, and the combined approach yields an accuracy of 78.96% with corresponding gains in precision, recall, and F1 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