[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120177-en":3,"doc-seo-120177-105":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":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},120177,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","WEB BASED HEART DISEASE PREDICTION MODEL USING MACHINE LEARNING TECHNIQUE - Research Article","Heart disease incidence is rising rapidly, making early prediction and efficient, precise diagnosis increasingly important. This study builds a web based heart disease prediction system that uses patients’ medical attributes to determine whether a patient is likely to be diagnosed with heart disease. Machine learning classifiers, including Logistic Regression and Naïve Bayes, are applied for prediction and classification, with the model designed to improve the accuracy of heart attack probability estimation. The proposed approach enhances medical decision support and helps reduce healthcare cost.","OPEN ACCESS  \nComputer Science & IT Research Journal  \nP-ISSN: 2709-0043, E-ISSN: 2709-0051  \nVolume 5, Issue 2, P.518-527, February 2024 DOI: 10.51594/csitrj.v5i2.837  \nFair East Publishers [Journal Homepage: ](Journal Homepage: www.fepbl.com/index.php/csitrj)[www.fepbl.com/index.php/csitrj](Journal Homepage: www.fepbl.com/index.php/csitrj)  \nWEB BASED HEART DISEASE PREDICTION MODEL USING MACHINE LEARNING TECHNIQUE  \nMusa Abubakar 1, Abba Hamman Maidabara2, Yusuf Musa Malgwi3, & Abdulrahman Mohammed4  \n1Department of Computer Science, Adamawa State Polytechnic,Yola Nigeria 2Maidugu street, OLD GRA, Maiduguri, Borno State, Nigeria 3Department of Computer Science, Modibbo Adama University, Yola, P.M.B. 2076 Yola,  \nAdamawa State, Nigeria.  \n4Department of Computer Science Federal Polytechnic Kaltungo, Gombe, Nigeria.  \n*Corresponding Author: Abba Hamman Maidabara  \n[Corresponding Author Email: ](Corresponding Author Email: h.abbahamman@gmail.com)[h.abbahamman@gmail.com](Corresponding Author Email: h.abbahamman@gmail.com)  \nArticle Received: 30-11-23 Accepted: 05-02-24 Published: 26-02-24  \nLicensing Details: Author retains the right of this article. The article is distributed under the terms of the  \nCreative Commons Attribution-NonCommercial 4.0 License  \n([http://www.creativecommons.org/licences/by-nc/4.0/](http://www.creativecommons.org/licences/by-nc/4.0/)) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the Journal open access page  \nABSTRACT  \nThe cases of heart diseases are increasing at a rapid rate and it’s very important to take precaution to predict any such diseases beforehand. This diagnosis is a difficult task [i.e. it](i.e. it) should be performed precisely and efficiently. The research paper mainly focuses on wen based heart disease prediction technique based on various medical attributes. Heart disease prediction system were prepared to predict whether the patient is likely to be diagnosed with a heart disease or not using the medical history of the patient. We used different algorithms of machine learning such as logistic regression and Naïve Bayes to predict and classify the patient with heart disease. A quite helpful approach was used to regulate how the model can be used to improve the accuracy of  \nprediction of Heart Attack in any individual. The strength of the proposed model was quiet satisfying and was able to predict evidence of having a heart disease in a particular individual by using Naïve Bayes and Logistic Regression which showed a good accuracy in comparison to the previously used classifier such as naive bayes etc. So a quiet significant amount of pressure has been lift off by using the given model in finding the probability of the classifier to correctly and accurately identify the heart disease. The Given heart disease prediction system enhances medical care and reduces the cost. This project gives us significant knowledge that can help us predict the patients with heart disease.  \nKeywords: Web Based, Heart, Disease, Prediction Model, Machine Learning.  \nINTRODUCTION  \nHeart disease is a broad term for several conditions affecting blood vessels, arteries, and other organs, leading to incorrect heart function. Since the SARS-CoV-2 virus disrupts the human respiratory system and attempts to lower the amount of oxygen in the lungs, it has a significant negative influence on heart health and may even result in heart destruction (Mijwil, [et.al](et.al) 2021) The development of athermanous plaques, aberrant lipid metabolism, and the buildup of lipids and other chemicals in the circulation in the coronary arteries are all indications of heart disease. It can result in luminal narrowing or occlusion, which can cause myocardial ischemia, oxygen deprivation, or necrosis that manifests as chest discomfort, tightness in the chest, myocardial infarction, and other symptoms.  \nHeart disease is t","cbCaisfEBzl6DvlH","https://ap.wps.com/l/cbCaisfEBzl6DvlH","pdf",539224,1,10,"English","en",105,"# Abstract\n# Introduction\n## Background and global burden of heart disease\n## Role of machine learning in healthcare\n## Epidemiology and risk factors","[{\"question\":\"What is the main objective of the web based heart disease prediction model?\",\"answer\":\"To predict whether a patient is likely to be diagnosed with heart disease using the patient’s medical history and attributes.\"},{\"question\":\"Which machine learning algorithms are used in the study?\",\"answer\":\"The study applies Logistic Regression and Naïve Bayes to classify and predict heart disease presence.\"},{\"question\":\"How does the proposed model support healthcare practice?\",\"answer\":\"By providing probability estimates that can improve decision making for early detection and help reduce overall medical cost.\"}]","WEB BASED HEART DISEASE PREDICTION MODEL USING MACHINE LEARNING TECHNIQUE - Research Article | PDF",1785728568,25,{"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},"web-based-heart-disease-prediction-model-using-machine-learning-technique-research-article","",{"@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/web-based-heart-disease-prediction-model-using-machine-learning-technique-research-article/120177/",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":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main objective of the web based heart disease prediction model?","Question",{"text":75,"@type":76},"To predict whether a patient is likely to be diagnosed with heart disease using the patient’s medical history and attributes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning algorithms are used in the study?",{"text":80,"@type":76},"The study applies Logistic Regression and Naïve Bayes to classify and predict heart disease presence.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed model support healthcare practice?",{"text":84,"@type":76},"By providing probability estimates that can improve decision making for early detection and help reduce overall medical cost.","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},[93,97,101,105,110,115,120,123,128,131,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]