DATA MINING APPROACH TO THE EVALUATION OF THE MOST COMMON DISEASES: K-MEANS CLUSTERING STUDY IN PUSTU LAPANG SEDAR IN 2023
DOI:
https://doi.org/10.31949/jmm.v5i1.12233Abstract
Disease pattern analysis is an important step in efforts to improve the quality of public health services. This study evaluates the patterns of diseases that frequently occur at PUSTU Lapang Sedar during 2023 using the K-Means Clustering data mining method. By utilizing WEKA software, the disease data was grouped into three clusters: dominant, moderate, and rare diseases, based on the number of occurrences. The clustering results show that ARI dominates with the highest frequency, followed by dyspepsia in the moderate cluster, and myalgia in the rare disease cluster. The results of this study help in more efficient resource allocation and more targeted planning of disease prevention programs. The K-Means Clustering approach enables data-driven disease mapping to support decision-making at the primary health facility level. In conclusion, the application of data mining in disease analysis makes an important contribution to the optimization of health services at PUSTU Lapang Sedar.
Keywords:
Data Mining, K-Means Clustering, Disease Evaluation, PUSTU, Lapang Sedar, WEKADownloads
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