[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119776-en":3,"doc-seo-119776-105":30,"detail-sidebar-cat-0-en-105":90},{"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},119776,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",7,"Healthcare","Machine Learning Algorithms Based Non-Alcoholic Fatty Liver Disease Prediction","Early-stage liver disease prediction is essential for timely intervention and prevention of severe complications. Non-alcoholic fatty liver disease (NAFLD) is a chronic condition whose early rigorous detection is challenging, yet accurate prediction supports better treatment decisions. This paper builds a machine-learning based NAFLD prediction framework using Decision Tree, Support Vector Machine, Random Forest, and Logistic Regression classifiers. Model performance is evaluated with accuracy metrics, with results indicating Random Forest as the most accurate for NAFLD patient prediction.","Machine Learning Algorithms Based Non  \nAlcoholic Fatty Liver Disease Prediction  \nBindu Bhargavi Munukuntla, Mrutyunjaya S Yalawar  \nAbstract: The early stage liver diseases prediction is an important health related research and using this kind of research easily can predict the diseases and take the remedies. The liver diseases are classified into different types such as liver cancer, liver tumor, fatty liver, hepatitis, cirrhosis etc. Non-Alcoholic Fatty Liver Disease is a kind of chronic disease which rigorous prediction is quite difficult at early stages. The prediction of fatty liver plays significant role in treating the disease and also constraining the next health consequences. This paper presents Machine Learning Algorithms based Non Alcoholic Fatty Liver Disease (NAFLD) prediction. The main objective of this project is to identify the potential factors causing NAFLD by using Machine Learning algorithms like Decision Tree (DT) classifier, Support Vector Machine (SVM) classifier, Random Forest (RF) classifier, Logistic regression (LR). Accuracy is used parameter for performance analysis evaluation. The findings of this paper show that random forest model accurately predicts a non-alcoholic fatty liver disease patient.  \nKeywords: Liver Disease, Classification, Machine Learning, NAFLD, Electronic Health Records.  \nI. INTRODUCTION  \nLiver disease is any disturbance of liver function that causes illness. The liver is responsible for many critical functions within the body and should it become diseasedor injured, the loss of those functions can cause significant damage to the body [1] . The development and the fundamental need of exchanging information between various academic disciplines provided an opportunity to make considerable progress in diagnosing and treating chronic disease. In this case, the research was carried out in the field of liver disorders and predicting the grades of fatty liver by using the blood test features instead of ultrasound images [2] . Non-alcoholic fatty liver disease (NAFLD) is a common and progressive disease that can lead to many liver diseases. Its prevalence has increased due to the emergence of obesity, diabetes, and  \nManuscript received on 20 July 2023 | Revised Manuscript received on 28 July 2023 | Manuscript Accepted on 15 September 2023 | Manuscript published on 30 September 2023.  \n*Correspondence Author(s)  \nBindu Bhargavi Munukuntla*, Department of Computer Science and Engineering, CMR Engineering College, Hyderabad (Telangana), India. E-mail: [bhargavimunukuntla26@gmail.com](bhargavimunukuntla26@gmail.com), ORCID ID:  \n0009-0004-7643-0370  \nMrutyunjaya S. Yalawar, Assistant Professor, Department of Computer Science and Engineering, CMR Engineering College, Hyderabad (Telangana), [India. E-mail: ](India. E-mail: mrutyunjaya.cmrec20@cmrec.ac.in)[mrutyunjaya.cmrec20@cmrec.ac.in](India. E-mail: mrutyunjaya.cmrec20@cmrec.ac.in)  \n© The Authors. Published by Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) . This is an open access article under the CC-BY-NC-ND license [http://creativecommons.org/licenses/by-nc-nd/4.0/](http://creativecommons.org/licenses/by-nc-nd/4.0/)  \nhypertension. In these diseases, liver damage is not related to alcohol consumption. As it can be fatal in some cases, there are many ways to catch this condition early so that it can be treated and healed quickly. Therefore, using machine learning classification algorithms (using various biological diseases of patients to help identify and predict the nature of NAFLD) is a good way to solve these problems, and in this project we use 6 features to identify this condition in patients [3] .  \nMetabolic syndrome, body mass index (BMI), gender, lipoprotein cholesterol (high and low), total and direct bilirubin, fibrosis formation, age, etc. The following risk factors have been found for NAFLD, such as This project will compare the performance of several of the above methods. Since NAFLD estimation is","cbCaipgJ75mNIyYR","https://ap.wps.com/l/cbCaipgJ75mNIyYR","pdf",272251,1,4,"English","en",105,"# Introduction\n## Problem background of liver disease and NAFLD\n## Machine learning approach and objectives\n# Literature Review","[{\"question\":\"Why is early prediction of non-alcoholic fatty liver disease (NAFLD) important?\",\"answer\":\"NAFLD can progress to other serious liver conditions. Early prediction helps support timely treatment and reduces the risk of future health consequences.\"},{\"question\":\"Which machine learning algorithms are used for NAFLD prediction in the study?\",\"answer\":\"The study uses Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR) classifiers.\"},{\"question\":\"What evaluation metric is used to compare model performance?\",\"answer\":\"Accuracy is used to assess and compare the performance of the prediction models.\"}]","Machine Learning Algorithms Based Non-Alcoholic Fatty Liver Disease Prediction | PDF",1785726260,10,{"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":85,"head_meta":87,"extra_data":89,"updated_unix":28},"machine-learning-algorithms-based-non-alcoholic-fatty-liver-disease-prediction","",{"@graph":36,"@context":84},[37,53,67],{"@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/healthcare/",3,{"item":52,"name":13,"@type":43,"position":21},"https://docshare.wps.com/document/machine-learning-algorithms-based-non-alcoholic-fatty-liver-disease-prediction/119776/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Why is early prediction of non-alcoholic fatty liver disease (NAFLD) important?","Question",{"text":74,"@type":75},"NAFLD can progress to other serious liver conditions. Early prediction helps support timely treatment and reduces the risk of future health consequences.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which machine learning algorithms are used for NAFLD prediction in the study?",{"text":79,"@type":75},"The study uses Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR) classifiers.",{"name":81,"@type":72,"acceptedAnswer":82},"What evaluation metric is used to compare model performance?",{"text":83,"@type":75},"Accuracy is used to assess and compare the performance of the prediction models.","https://schema.org",{"og:url":52,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,117,122,127,130,133],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":115,"slug":116},40,"healthcare",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":119,"show_sort_weight":120,"slug":121},8,"Research & Report",30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":29,"slug":132},"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]