The Use of K-Means Algorithm in Clustering Diabetes Mellitus Patients Based on Clinical Parameters at Brebes Community Health Center
DOI:
https://doi.org/10.51903/elkom.v18i1.2947Keywords:
Diabetes Mellitus, Data Mining, K-MeansAbstract
Diabetes Mellitus is one of the chronic diseases with an increasing number of cases each year, including in the Brebes Health Center area. The large number of patients with diverse clinical conditions necessitates a method to group patients based on the severity of their condition. This study aims to apply the K-Means algorithm in the clustering process of Diabetes Mellitus patients using several clinical parameters, namely Fasting Blood Glucose (GDP), HbA1c levels, Total Cholesterol (CHOL), and systolic and diastolic blood pressure. The approach used in this study is quantitative descriptive, employing data mining methods based on the K-Means algorithm. The data used was obtained from medical records at the Brebes Health Center. The clustering process resulted in three groups: low, medium, and high-risk categories. The results showed that the K-Means algorithm is capable of accurately grouping patient data according to severity levels. These results were then visualized through a web-based system, designed to assist health center staff in analyzing patient conditions and supporting more effective medical decision-making.
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