[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122318-en":3,"doc-seo-122318-105":29,"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},122318,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Prediction of Hypertension Patients with Machine Learning Algorithm - Hypertension Risk Prediction","Hypertension, recognized as the “silent killer,” shows continuously rising prevalence worldwide, with many cases remaining undiagnosed and treatment adherence remaining low. Because symptoms are often absent or hard to detect, timely screening is essential to avoid serious complications. This study builds a hypertension prediction system using Decision Tree and Random Forest machine learning methods, trained on clinical data such as age, medical history, and lifestyle. Reported performance reaches 99.6% accuracy for Decision Tree and 99.5% for Random Forest, supporting faster, more accurate decision-making for healthcare intervention planning.","Research Article  \nPrediction of Hypertension Patients with Machine Learning Algorithm  \n1Eko Priyono  \nNusa Mandiri University And BMKG Jakarta, Indonesia [14220040@nusamandiri.ac](14220040@nusamandiri.ac)  \nA R T I C L E I N F O  \nArticle History  \nReceived: 07/02/2025  \nAccepted: 02/04/2025  \nPublished:05/06/2025 This is an open-access article under the CC BY 4.0 license: [http://creativecommons.or](http://creativecommons.or)[g/licenses/by/4.0/](g/licenses/by/4.0/)  \nABSTRACT  \nHypertension, known as the \"silent killer,\" is one of the leading causes of global mortality, with a steadily increasing prevalence. Worldwide, the prevalence of hypertension reaches approximately 30%, with only 50% of cases being diagnosed and a low level of treatment adherence. Hypertension symptoms are often invisible, making early detection crucial to preventing serious complications. This paper aims to develop a hypertension prediction system using the Decision Tree and Random Forest algorithms, which are machine learning techniques used to solve classification and regression problems. These algorithms can predict hypertension risk based on clinical data, such as age, medical history, and lifestyle. The findings of this paper indicate that the Decision Tree and Random Forest algorithms are effective in predicting hypertension risk, achieving accuracies of 99.6% and 99.5%, respectively. This prediction system can provide fast and accurate information, assisting healthcare professionals in designing appropriate intervention strategies while also supporting better medical decision-making.  \nKeywords: Decision Tree, Deteksi Dini, Hypertension, Machine Learning, Random Forest ensemble, Decision Tree classifier  \n1. INTRODUCTION  \nHypertension, or high blood pressure, is among the main causes of death worldwide, often referred to as the \"silent killer\" due to its asymptomatic but deadly nature [1] . Globally, hypertension has caused approximately 9.4 million deaths, with the highest prevalence found in the 31-44 age group (20%), age groups of 45–54 (35%), and 55–64 (45%) . Total hypertension prevalence 30%, only 50% have been diagnosed, and 40% of those diagnosed do not regularly take medication [2]. Additionally, 30% of patients fail to adhere to treatment schedules, reflecting a lack of awareness and proper management among individuals with hypertension [3] .  \nCommon symptoms of hypertension include headaches, dizziness, nausea, vomiting, neck pain, fatigue, anxiety, shortness of breath, nosebleeds, and loss of consciousness. Although previously more common in older adults, hypertension is now increasingly observed among younger generations. Risk factors include gender, age, genetics, stress, obesity, alcohol consumption, smoking, high salt intake, absence of exercise, as well as a history of diabetes or kidney issues. The rising prevalence of hypertension highlights the need for early detection to reduce the risk of severe complications [4], [5] .  \nMachine learning algorithms offer a solution for detecting and predicting hypertension risk more effectively and accurately. Algorithms such as Random Forest, Decision Tree, and Logistic Regression can assist healthcare professionals in predicting hypertension risk based on clinical data. These algorithms process information from various risk factors, including age, medical history, and lifestyle, to provide accurate predictions about an individual's hypertension status [6], [7], [8] .  \nStudies have shown that Random Forest and Decision Tree algorithms are highly effective in predicting diseases, including hypertension. With high accuracy rates of 99.6% for Decision Tree and 99.5% for Random Forest, these algorithms can enhance understanding and provide valuable insights for hypertension risk prediction [9], [10] . The prediction results based on machine learning algorithms can provide fast and accurate information, assisting in the formulation of effective prevention strategies. Thus, the application","cbCaigO1AunAiyC2","https://ap.wps.com/l/cbCaigO1AunAiyC2","pdf",605525,1,"English","en",105,"# INTRODUCTION\n## Key health problem and risk factors\n## Role of machine learning\n# TECHNIQUES METHOD\n## Dataset\n## Model development and workflow","[{\"question\":\"Why is early detection of hypertension important?\",\"answer\":\"Hypertension often has no obvious symptoms, making early detection critical to prevent serious complications and reduce long-term risk.\"},{\"question\":\"Which machine learning algorithms are used in this study?\",\"answer\":\"The study develops a prediction system using the Decision Tree algorithm and the Random Forest algorithm.\"},{\"question\":\"What kind of clinical data is used for prediction?\",\"answer\":\"The system predicts hypertension risk based on clinical data including age, medical history, and lifestyle factors.\"}]","Prediction of Hypertension Patients with Machine Learning Algorithm - Hypertension Risk Prediction | PDF",1785809982,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"prediction-of-hypertension-patients-with-machine-learning-algorithm-hypertension-risk-prediction","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/prediction-of-hypertension-patients-with-machine-learning-algorithm-hypertension-risk-prediction/122318/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Why is early detection of hypertension important?","Question",{"text":74,"@type":75},"Hypertension often has no obvious symptoms, making early detection critical to prevent serious complications and reduce long-term risk.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which machine learning algorithms are used in this study?",{"text":79,"@type":75},"The study develops a prediction system using the Decision Tree algorithm and the Random Forest algorithm.",{"name":81,"@type":72,"acceptedAnswer":82},"What kind of clinical data is used for prediction?",{"text":83,"@type":75},"The system predicts hypertension risk based on clinical data including age, medical history, and lifestyle factors.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":28,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":28,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]