Improving Coronary Heart Disease Detection Using K-Means Clustering Techniques - Junaidy Sanger; Liza Wikarsa; Angelica Taulu (Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas, 2025)
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.