[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124142-en":3,"doc-seo-124142-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},124142,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Evaluating machine learning models for predictive analytics of liver disease detection using healthcare big data - Research comparison","Liver diseases cause widespread morbidity and mortality worldwide, and early detection enables timely intervention that can slow progression to severe outcomes such as cirrhosis or liver cancer. The study compares three machine learning approaches for predictive analytics using a healthcare big-data set of 32,000 records. A preprocessing step handles missing or corrupted values to preserve data integrity. Model evaluation shows Random Forest delivers the strongest performance, with accuracy 97.3%, precision 97%, recall 96%, and F1-score 95%.","Evaluating machine learning models for predictive analytics of liver disease detection using healthcare big data  \nOsama Mohareb Khaled1, Ahmed Zakareia Elsherif1,2, Ahmed Salama1, Mostafa Herajy1,  \nElsayed Elsedimy3  \n1Department of Mathematics and Computer Science, Faculty of Science, Port Said University, Port Said, Egypt 2Department of Basic Sciences, Higher Institute of Administrative Sciences, El-Menzala, Egypt 3Department of Information Technology Management, Faculty of Management Technology and Information Systems,  \nPort Said University, Port Said, Egypt  \n\n| Article history:\u003Cbr>Received Mar 18, 2024 Revised Sep 16, 2024 Accepted Oct 1, 2024 | Liver diseases rank among the most prevalent health issues globally, causing significant morbidity and mortality. Early detection of liver diseases allows for timely intervention, which can prevent the progression of such diseases to more severe stages such as cirrhosis or liver cancer. To this end, many machine learning models have been previously developed to early predict liver diseases among potential patients. However, each model has its accuracy and performance limitations. In this paper, we present a comprehensive comparison of three different machine learning models that can be employed to enhance the prediction and management of liver diseases. We utilize a big data set of 32,000 records to evaluate the performance of each model. First, we implement a preprocessing technique to rectify missing or corrupt data in liver disease datasets, ensuring data integrity. Afterwards, we compare the performance of three machine models: k-nearest neighbors (KNN), gaussian naive Bayes (Gaussian NB) and random forest (RF) . We concluded that the RF algorithm demonstrates superior performance in our evaluation, excelling in both predictive accuracy and the ability to classify patients accurately regarding the presence of liver disease. Our results show that RF outperforms other models based on several performance metrics including accuracy: 97.3%, precision: 97%, recall: 96%, and F1-score: 95% .\u003Cbr>This is an open access article under the CC BY-SA license.\u003Cbr> |\n| --- | --- |\n| Keywords:\u003Cbr>Big data\u003Cbr>Gaussian naive Bayes K-nearest neighbors\u003Cbr>Liver disease patient dataset Random forest |  |\n\nCorresponding Author:  \nAhmed Zakareia Elsherif  \nDepartment of Mathematics and Computer Science, Faculty of Science, Port Said University El Zohour District, Port Said Governorate, 8560001, Egypt  \nEmail: [ahmad.elsherif@sci.psu.edu.eg](ahmad.elsherif@sci.psu.edu.eg)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nIn abnormal liver function (also called liver disease), the liver's effectiveness is severely diminished if only 25% of it is still working while the other 75% is damaged [1], [2] . Predicting liver disease at an early stage allows for timely intervention, which can prevent the disease from progressing to more severe stages. Early treatment can halt or slow down the disease, improving patient outcomes. To this end, artificial intelligence approaches, particularly machine learning models, offer promising solutions to many classification and prediction problems, including liver disease [3]–[10] .  \nMany approaches were introduced to predict and classify liver diseases using machine learning [11]–[19]. Choudhary et [al](al. in)[. in](al. in) [20] proposed a machine learning model for liver disease prediction. This study helps improve liver disease diagnosis by validating patient parameters and genome expression,  \nanalyzing computer algorithms, and offering ways to increase efficiency. The authors employed Scikit-learn, Numpy, Pandas, and Seaborn libraries to create machine learning models that achieve accuracy of 70.5% using logistic regression and accuracy of 65% using the vector machine approaches. Besides, an intelligent approach has been introduced to predicted liver illness by Veeranki and Varshney in [21] . They introduced a novel bioinformatics model that has been applied to patient ","cbCaiagi83iFfVp6","https://ap.wps.com/l/cbCaiagi83iFfVp6","pdf",833917,1,13,"English","en",105,"# Article Info Abstract\n## 1. Introduction\n## Related Work and Machine Learning Approaches","[{\"question\":\"Why is early liver disease detection important?\",\"answer\":\"Early detection supports timely intervention, helping prevent progression to more severe stages such as cirrhosis or liver cancer and improving patient outcomes.\"},{\"question\":\"Which preprocessing step is applied before model evaluation?\",\"answer\":\"A preprocessing technique is used to correct missing or corrupted data in liver disease datasets to ensure data integrity.\"},{\"question\":\"Which machine learning model performs best in the study?\",\"answer\":\"Random Forest (RF) shows superior performance, achieving accuracy 97.3% and strong precision, recall, and F1-score values compared with KNN and Gaussian Naive Bayes.\"}]","Evaluating machine learning models for predictive analytics of liver disease detection using healthcare big data - Research comparison | PDF",1785820685,33,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"evaluating-machine-learning-models-for-predictive-analytics-of-liver-disease-detection-using-healthcare-big-data-research-comparison","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/evaluating-machine-learning-models-for-predictive-analytics-of-liver-disease-detection-using-healthcare-big-data-research-comparison/124142/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is early liver disease detection important?","Question",{"text":76,"@type":77},"Early detection supports timely intervention, helping prevent progression to more severe stages such as cirrhosis or liver cancer and improving patient outcomes.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which preprocessing step is applied before model evaluation?",{"text":81,"@type":77},"A preprocessing technique is used to correct missing or corrupted data in liver disease datasets to ensure data integrity.",{"name":83,"@type":74,"acceptedAnswer":84},"Which machine learning model performs best in the study?",{"text":85,"@type":77},"Random Forest (RF) shows superior performance, achieving accuracy 97.3% and strong precision, recall, and F1-score values compared with KNN and Gaussian Naive Bayes.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]