Abstract
Heart disease is a prominent global health concern, necessitating early identification and patient grouping for effective management. This study employs the K-Means clustering algorithm with a medical dataset of 303 patients, encompassing various attributes. These include Age, Gender, Chest Pain Type, Blood Pressure, Serum Cholesterol Level, Fasting Blood Sugar, Resting Electrocardiographic Results, Maximum Heart Rate, Angina, ST Depression, and Slope of the ST Segment. The goal is to categorize patients into four clusters based on chest pain types, a crucial symptom indicating disease severity. The computation concludes after the sixth iteration, revealing Cluster 1 (27 patients), Cluster 2 (135 patients), Cluster 3 (15 patients), and Cluster 4 (126 patients). Collaborative analysis with medical experts highlights that Cluster 1, mainly comprising older males, exhibits high-risk indicators. While this grouping aids in personalized treatment strategy development, further clinical validation involving more experts and datasets is imperative for enhanced reliability.
Published Version
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