[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120636-en":3,"doc-seo-120636-105":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},120636,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Improved Accuracy for Heart Disease Diagnosis Using Machine Learning Techniques","Accurate diagnosis of cardiovascular diseases is essential because millions of lives are lost annually due to heart-related conditions worldwide. This study targets heart disease identification before patients reach expert doctors by using machine learning models that learn expert-like patterns from clinical measurements. The work classifies heart disease from 13 vital parameters taken from the Cleveland dataset, including age, sex, chest pain type, fasting blood sugar, and resting ECG. Models including linear regression, back propagation neural networks, SVM, and k-nearest neighbor are evaluated against standard Cleveland-based studies, and results report perfect correctness and robust SVM performance on test data.","Journal of Informatics and Web Engineering  \nVol. 4 No. 1 (February 2025) eISSN: 2821-370X  \nImproved Accuracy for Heart Disease Diagnosis Using Machine Learning Techniques  \nNeeraja Joshi1, Tejal Dave2*  \n1,2Sarvajanik University, Athwalines, Surat, Gujarat 395001, India.  \n*corresponding author: ([tejal.dave@scet.ac.in](tejal.dave@scet.ac.in); ORCiD: 0000-0003-2773-9748)  \nAbstract-Accurate diagnosis of cardiovascular diseases (CVDs) is vital as people face many health issues due to CVD. Worldwide, more than 17 million people lose their lives each year due to CVD. This work primarily focuses on diagnosing heart disease before an explicit visit to the expert doctor. Machine learning-based systems have been found helpful in all applications, including medical ones, as they can learn human-like expert knowledge and utilize it subsequently. This work performs the classification of heart disease utilizing the subject's vital parameters. Ordinary people and patients need help understanding pathological laboratory results available after Testing and have to wait till they visit expert doctors for inference. In this paper, traditional methods like linear regression to various machine learning-based systems, including back propagation neural network, support vector machine (SVM), and k-nearest neighbor, are developed for heart disease classification. The proposed system (i) takes 13 vital parameters, including age, sex, chest pain type, fasting blood sugar, resting ECG, etc., as available from the Cleveland database, (ii) processes them with tuned machine learning systems, and (iii) transforms sensor inputs to stroke stage classification. To ascertain the proposed system's efficacy, all methods' performances are compared with similar work performed on the same standard-Cleveland database. Simulation results show 100 percent correct diagnosis and the robustness ofSVM-based approaches for test data.  \nKeywords—Neural Network, Normalization, Classification, Support Vector Machine (SVM), k-Nearest Neighbor (KNN)  \nReceived: 11 September 2024; Accepted: 09 November 2024; Published: 16 February 2025 This is an open access article under the CC BY-NC-ND 4.0 license.  \n1. INTRODUCTION  \nAccording to the WHO report [1], cardiovascular diseases (CVDs) are one of the major causes of death worldwide, with approximately 17.9 million deaths reported each year. The same report [1] adds that some common symptoms like obesity, hypertension, hyperglycemia, and high cholesterol increase the chances of heart-related diseases. New York Heart Association (NYHA) has derived functional Classification [2] for heart failure according to the severity of their symptoms. Medical history data indicate measurements of clinical parameters for the subject's body and are essential to diagnosing particular details about a given disease. Medical data can extract vital information about diseases from specific stored measurements.  \nMachine learning approaches have found many uses in domains like surveillance, sensor networks, mobile networks, health care, robotics, etc., for various tasks, including identifying earlier undetected patterns and generating control actions from various systems based on feedback received [3, 4]. In medical imaging, machine learning techniques are used to understand patients' characteristics for model estimation and classification, e.g., Normal and severe heart diseases. Once appropriately trained, machine learning techniques help efficiently classify a given dataset [5] .  \nHowever, despite the impressive advances in machine learning, several problems still need to be solved. In general, difficulties arise because of uncertainties in the absence of adequate prior information about the surroundings, data range-related limitations and calibrations of cameras or sensors, adverse observation of data being acquired, etc. These all may lead to unpredictability in the values of input variables given to the intelligent systems. Other problems are rel","cbCaiatxt0ngoeMZ","https://ap.wps.com/l/cbCaiatxt0ngoeMZ","pdf",646375,1,11,"English","en",105,"# Introduction\n## Cardiovascular disease burden and symptoms\n## Role of machine learning in healthcare\n## Challenges in applying machine learning\n## Related work and prior classifiers","[{\"question\":\"What problem does this paper address?\",\"answer\":\"The paper addresses early and accurate diagnosis of heart disease by using machine learning techniques before patients receive specialist interpretation.\"},{\"question\":\"Which inputs does the proposed system use?\",\"answer\":\"It uses 13 vital parameters such as age, sex, chest pain type, fasting blood sugar, and resting ECG from the Cleveland database.\"},{\"question\":\"Which machine learning methods are compared and how is performance evaluated?\",\"answer\":\"The study develops and compares methods including linear regression, back propagation neural network, support vector machine (SVM), and k-nearest neighbor, and evaluates them on the standard Cleveland dataset used in prior work.\"}]","Improved Accuracy for Heart Disease Diagnosis Using Machine Learning Techniques | PDF",1785731028,28,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"improved-accuracy-for-heart-disease-diagnosis-using-machine-learning-techniques","",{"@graph":36,"@context":85},[37,54,68],{"@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/improved-accuracy-for-heart-disease-diagnosis-using-machine-learning-techniques/120636/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does this paper address?","Question",{"text":75,"@type":76},"The paper addresses early and accurate diagnosis of heart disease by using machine learning techniques before patients receive specialist interpretation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which inputs does the proposed system use?",{"text":80,"@type":76},"It uses 13 vital parameters such as age, sex, chest pain type, fasting blood sugar, and resting ECG from the Cleveland database.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning methods are compared and how is performance evaluated?",{"text":84,"@type":76},"The study develops and compares methods including linear regression, back propagation neural network, support vector machine (SVM), and k-nearest neighbor, and evaluates them on the standard Cleveland dataset used in prior work.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]