[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123502-en":3,"doc-seo-123502-105":30,"detail-sidebar-cat-0-en-105":95},{"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},123502,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Comparative Analysis of Machine Learning Models for Early Heart Disease Diagnosis","Early heart disease detection is critical because cardiovascular diseases remain a leading cause of death worldwide. This research evaluates machine learning performance for heart disease prediction using the UCI Cleveland Heart Disease dataset, leveraging clinical variables such as age, sex, blood pressure, cholesterol, and chest-pain type. Data preprocessing includes handling missing values, encoding categorical features, and scaling. An SVM model is optimized via grid search and cross-validation, achieving 86.41% test accuracy and outperforming logistic regression and random forest. The work supports practical clinical decision support and outlines further improvements through larger and more diverse datasets.","Comparative Analysis of Machine Learning Models for Early Heart Disease Diagnosis  \nRajeshree Khande 1 , Walid Ayadi2 , Nikita Bhandhari 1 , Yasser Farhat3,* , P.S. Metkewar4 , Sharvari R. Shukla5 , Aafaq A. Rather5 and Mushtaq A. Lone6  \n1Balaji Institute of Technology & Management, Sri Balaji University, Pune, India 2Mechatronics and Intelligent Systems, Abu Dhabi Polytechnic, UAE 3Academic Support Department, Abu Dhabi Polytechnic, Abu Dhabi, UAE  \n4School of Computer Science and Engineering, Dr Vishwanath Karad MIT World Peace University, Pune, India  \n5Symbiosis Statistical Institute, Symbiosis International (Deemed University), Pune, India 6Departmet of Statistics, FOH, SKUAST–K, P. O. Box 190025, J&K, India  \nAbstract: Heart disease remains among the leading causes of death worldwide, and its early detection ability can be the difference between life and death. In this research, we investigate the capability of machine learning—namely Support Vector Machines (SVM)—to predict the occurrence of heart disease based on regular clinical information. We used the Cleveland Heart Disease dataset, which contains critical patient data like age, gender, blood pressure, cholesterol level, type of chest pain, and other crucial health factors. Prior to creating our model, we pre-processed and cleaned the data by dealing with missing values, changing categorical variables into numerical form, and scaling the features for uniformity. We then optimized the SVM model using grid search and cross-validation to make it run at its optimal level.  \nThe resulting model had an accuracy of 86.41% in the test set and performed better than other popular models such as logistic regression and random forest.  \nThe significant about this work is the potential for applying it in practical situations. An SVM-based program such as this could be a second opinion for physicians or integrated into early diagnostic tools—most helpful in clinics with limited access to specialists. It's progress toward smarter, data-driven healthcare that enables faster and more precise diagnoses.  \nThere's still potential for expansion, using bigger, more varied datasets or incorporating real-time patient information could further enhance the model. But this research demonstrates that with the proper data and methodology, machine learning can be a useful tool in the early diagnosis of heart disease.  \nKeywords: Heart Disease Prediction, Machine Learning, Support Vector Machine (SVM), Clinical Decision Support, Feature Engineering.  \n1. INTRODUCTION  \nCardiovascular diseases (CVDs), encompassing a range of heart and blood vessel disorders, continue tobe the global leading cause of death. The World Health Organization estimates that CVDs resulted in 17.9 million deaths in 2019 alone . These statistics highlight the need for new approaches to improve early detection and timely treatment. Traditional diagnostic methods, while effective, are generally reliant on invasive assessment or limited to predicting, for instance, symptomatic rather than asymptomatic status . The incorporation of computational methodologies into the pipeline of diagnosis thus is becoming increasingly widespread. The healthcare sector is currently witnessing the advent of an explosive technology called machine learning (ML),  \n*Address correspondence to this author at the Academic Support Department,  \nAbu Dhabi Polytechnic, Abu Dhabi, UAE; E-mail: [farhat.yasser.1@gmail.com](farhat.yasser.1@gmail.com)  \nwhich holds the potential to act as an ideal tool for predicting diseases especially heart diseases at an early stage. ML algorithms are currently being used to analyse the huge amount of data that come from patients, including both demographic and clinical data . These algorithms discover meaningful underlying patterns and make us of these patterns to act in a predictive manner. When we talk about heart disease prediction, we're largely referring to risk stratification . By using ML, we're attempting to id","cbCaije3wunXw2GA","https://ap.wps.com/l/cbCaije3wunXw2GA","pdf",489866,1,11,"English","en",105,"# Introduction\n# Related Work\n# Methodology\n## Data preprocessing\n## Model optimization\n# Results and Discussion\n# Conclusion and Future Work","[{\"question\":\"What machine learning approach is used for early heart disease diagnosis in this study?\",\"answer\":\"The study uses Support Vector Machines (SVM) to predict heart disease occurrence based on regular clinical information.\"},{\"question\":\"Which dataset and features are used to build the models?\",\"answer\":\"Models are trained on the Cleveland Heart Disease dataset, using patient attributes such as age, gender, blood pressure, cholesterol level, chest-pain type, and other clinical factors.\"},{\"question\":\"How was the SVM model improved and evaluated?\",\"answer\":\"The SVM model was optimized using grid search and cross-validation after preprocessing steps like missing-value handling, categorical encoding, and feature scaling.\"},{\"question\":\"What performance result does the proposed model achieve compared with other models?\",\"answer\":\"The resulting SVM model reaches 86.41% accuracy on the test set and performs better than logistic regression and random forest.\"}]","Comparative Analysis of Machine Learning Models for Early Heart Disease Diagnosis | 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machine learning approach is used for early heart disease diagnosis in this study?","Question",{"text":75,"@type":76},"The study uses Support Vector Machines (SVM) to predict heart disease occurrence based on regular clinical information.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which dataset and features are used to build the models?",{"text":80,"@type":76},"Models are trained on the Cleveland Heart Disease dataset, using patient attributes such as age, gender, blood pressure, cholesterol level, chest-pain type, and other clinical factors.",{"name":82,"@type":73,"acceptedAnswer":83},"How was the SVM model improved and evaluated?",{"text":84,"@type":76},"The SVM model was optimized using grid search and cross-validation after preprocessing steps like missing-value handling, categorical encoding, and feature scaling.",{"name":86,"@type":73,"acceptedAnswer":87},"What performance result does the proposed model achieve compared with other models?",{"text":88,"@type":76},"The 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