Penerapan Algoritma k-Means dalam Pengelompokan Data Pasien Diabetes
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Abstract
Diabetes mellitus is a chronic metabolic disease with an increasing prevalence and the potential to cause serious complications if not properly managed. One of the challenges in diabetes management is effectively grouping patients based on clinical characteristics and health risk levels. This study aims to apply the K-Means algorithm as an unsupervised learning method to cluster diabetes patient data in order to identify patterns and similarities in patient risk profiles. The dataset used consists of training data (Training.csv) and testing data (Testing.csv) with numerical attributes including Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, Diabetes Pedigree Function, and Age. The research stages include data loading, data preprocessing, implementation of the K-Means algorithm, application of the model to testing data, and evaluation of the clustering results. Clustering quality was evaluated using the Davies-Bouldin Index and Average Within Centroid Distance to measure cluster compactness and separation. The results show that the K-Means algorithm is capable of forming relatively homogeneous clusters of diabetes patients with good inter-cluster separation, as indicated by a Davies-Bouldin Index value of −0.727. These findings suggest that the K-Means method is effective for clustering diabetes patients based on clinical characteristics and can provide valuable insights to support risk analysis and the development of data-driven clinical decision support systems.
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