Clustering the Severity Levels of Hypertensive Patients Based on Blood Pressure using the K-Means Method

Lia Anggraini

doi:10.59934/jaiea.v6i1.2821

Authors

Lia Anggraini STMIK Kaputama

DOI:

https://doi.org/10.59934/jaiea.v6i1.2821

Keywords:

Data Mining, Hypertension, K-Means Clustering, MATLAB, Medical Records

Abstract

Hypertension is a noncommunicable disease that is on the rise at the Anugrah Binjai Primary Care Clinic; however, the use of patient medical records has so far been limited to administrative purposes. This study aims to objectively and systematically classify the severity of hypertension based on data using data mining techniques. The algorithm applied is K-Means Clustering, utilizing the variables of gender, age, and systolic blood pressure from patients’ medical records. The clustering process was processed and tested using the MATLAB application through Euclidean distance calculations until convergence was achieved. The results of the study using a three-cluster scenario produced a structured division of patient groups, namely Cluster 1 (moderate severity) with 151 data points, Cluster 2 (high severity) with 151 data points, and Cluster 3 (low severity) with 215 data points. An evaluation of clustering quality using cluster variance yielded values of 0.9359 for 3 clusters, 0.8412 for 4 clusters, and 0.7132 for 5 clusters. These results demonstrate that the K-Means method is capable of mapping the severity patterns of hypertensive patients to assist clinics in designing targeted educational programs and medical management.

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