[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290723-105":59,"doc-detail-290723-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","study-of-rf-and-svm-machine-learning-model-to-predict-heart-disease","Study of RF and SVM Machine Learning Model to Predict Heart Disease","","Heart disease remains one of the leading causes of mortality worldwide, making early and accurate diagnosis essential for preventing severe complications. Recent advancements in machine learning have enabled clinicians to analyze complex patient data more effectively than traditional diagnostic approaches. This study evaluates Random Forest and Support Vector Machine models using a curated clinical dataset and reports RF accuracy of 100% and SVM accuracy of 98.87%. SHAP and LIME are integrated for transparent, clinically meaningful explanations.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/study-of-rf-and-svm-machine-learning-model-to-predict-heart-disease/290723/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/study-of-rf-and-svm-machine-learning-model-to-predict-heart-disease/290723.png","ImageObject",300,407,{"name":92,"@type":93},"Miles","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-18","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Which models are evaluated for heart disease prediction in this study?","Question",{"text":112,"@type":113},"The study evaluates two machine learning models: Random Forest (RF) and Support Vector Machine (SVM).","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What dataset and preprocessing approach are used?",{"text":117,"@type":113},"The system uses a curated clinical dataset compiled from multiple public datasets, with cleaning for missing/inconsistent values. Categorical variables are converted using one-hot encoding, and numerical variables are normalized using z-score scaling.",{"name":119,"@type":110,"acceptedAnswer":120},"How does the study make model predictions interpretable?",{"text":121,"@type":113},"It integrates interpretability tools SHAP and LIME, using their explanations to clarify how input features influence the model decisions for clinicians.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},290723,1789642444,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":29,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":129,"read_time":143,"preview_page_count":4},13056703019404,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","Study of RF and SVM Machine Learning Model to Predict Heart Disease  \nShubham P. Jain*, Aruna M.  \nDepartment of Master of Computer Applications, Dayananda Sagar Academy of Technology and Management, Bengaluru, Karnataka, India  \n*Email: [Shubhampjain2002@gmail.com](Shubhampjain2002@gmail.com)  \nAbstract  \nHeart disease remains one of the leading causes of mortality worldwide, making early and accurate diagnosis essential for preventing severe complications. Recent advancements in machine learning have enabled clinicians to analyze complex patient data more effectively than traditional diagnostic approaches. This study evaluates two widely used machine learning models Random Forest (RF) and Support Vector Machine (SVM) for predicting heart disease using a curated clinical dataset. RF achieved an accuracy of 100%, while SVM achieved 98.87% . The study also integrates SHAP and LIME interpretability tools to provide transparent, clinically meaningful explanations. This combined focus on accuracy and explainability distinguishes the study from existing literature.  \nKeywords  \nRF Algorithm, SVM Algorithm, Model Interpretability Tools like SHAP and LIME  \nIntroduction  \nHeart disease remains one of the leading causes of death worldwide and continues to place a substantial burden on healthcare systems. Early diagnosis is critical, yet traditional evaluation techniques struggle to interpret large, heterogeneous patient datasets. Machine learning models have emerged as powerful tools capable of extracting complex relationships from clinical data. Prior studies have demonstrated strong predictive performance using Random Forest (RF) and Support Vector Machine (SVM) models; however, many lack sufficient interpretability, limiting clinical adoption. This study addresses these shortcomings by combining high-performing models with interpretability tools, aiming to deliver both accuracy and transparency.  \nSubmission: 2 9 September 2 0 2 5 ; Acceptance: 8 December 2025; Available online: December 2025 Copyright: © 2025. All the authors listed in this paper. The distribution, reproduction, and any other usage of the content of this paper is permitted, with credit given to all the author(s) and copyright owner(s) in accordance to common academic practice. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, as stated in the website: [https://creativecommons.org/licenses/](https://creativecommons.org/licenses/by/4.0/)[by/4.0/](https://creativecommons.org/licenses/by/4.0/)  \nMethodology  \n• System Overview  \nThe designed system aims to detect heart disease risk using patient clinical attributes compiled from multiple public datasets combined to form a diverse and representative dataset. Two leading and well-known machine learning models, RF and SVM, unified data using consistent preprocessing steps. This design facilitates fair and meaningful models' predictive performance. To make predictions understandable and trustworthy for medical practitioners, the system incorporates advanced model interpretability methods like SHAP and LIME. Tools' input features influence the models' decisions, thereby enhancing transparency. System Architecture as stated in figure 1 consists of data acquisition, preprocessing, model training, evaluation and Interpretability. The function is as per detailed below.  \nFigure 1: System Architecture  \n• Data Acquisition  \nThe system sources data containing key patient as age, gender, blood pressure readings, cholesterol values, and symptoms like exercise-induced angina.  \n• Preprocessing  \nThe initial step involves cleaning the dataset to address any missing or inconsistent values. Categorical variables, such as types of chest pain, are converted into a binary format understandable by the machine using one-hot encoding. Numerical data undergo normalization through z-score scaling, which adjusts values to have a mean of zero and a standard dev","cbCaibfVva2JWBPC","https://ap.wps.com/l/cbCaibfVva2JWBPC","pdf",184428,"English","# Abstract\n# Keywords\n# Introduction\n# Methodology\n## System Overview\n## Data Acquisition\n## Preprocessing\n## Model Training\n## Evaluation\n## Interpretability\n# Implementation","[{\"question\":\"Which models are evaluated for heart disease prediction in this study?\",\"answer\":\"The study evaluates two machine learning models: Random Forest (RF) and Support Vector Machine (SVM).\"},{\"question\":\"What dataset and preprocessing approach are used?\",\"answer\":\"The system uses a curated clinical dataset compiled from multiple public datasets, with cleaning for missing/inconsistent values. Categorical variables are converted using one-hot encoding, and numerical variables are normalized using z-score scaling.\"},{\"question\":\"How does the study make model predictions interpretable?\",\"answer\":\"It integrates interpretability tools SHAP and LIME, using their explanations to clarify how input features influence the model decisions for clinicians.\"}]","Study of RF and SVM Machine Learning Model to Predict Heart Disease | PDF",15]