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All Journal Jurnas Nasional Teknologi dan Sistem Informasi
Angelica Taulu
Program Studi Teknik Informatika, Fakultas Teknik, Universitas Katolik De La Salle Manado
Computer Science & IT Decision Sciences, Operations Research & Management

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Improving Coronary Heart Disease Detection Using K-Means Clustering Techniques Junaidy Sanger; Liza Wikarsa; Angelica Taulu
Jurnal Nasional Teknologi dan Sistem Informasi Vol 11 No 2 (2025): Agustus 2025
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v11i2.2025.107-117

Abstract

The heart is a crucial organ in the cardiovascular system, playing a key role in blood circulation and supplying oxygen and nutrients to the body. Cardiovascular diseases, particularly coronary heart disease (CHD), are the leading cause of death worldwide. In Indonesia, especially in North Sulawesi, the high prevalence of CHD is indicative of the effects of an unhealthy lifestyle. This study employs the K-Means clustering method to identify the early risk of CHD based on eight common symptoms, including chest pain, nausea, shortness of breath, heartburn, a history of hypertension, obesity, diabetes, and genetics. This innovative approach integrates these early warning signs and categorizes the risk into three groups: low CHD risk (C1), moderate CHD risk (C2), and high CHD risk (C3). The detection results are provided based on responses collected through a questionnaire within an application, aiming to raise awareness of CHD and encourage users to seek further health evaluations and adopt healthier lifestyles.
Co-Authors Junaidy Budi Sanger Wikarsa, Liza