Analisis Pola Risiko Stroke pada Data Pasien Menggunakan Model K-Means Clustering Berbasis RapidMiner
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Abstract
This study aims to identify patterns of stroke risk among patients by applying the K-Means clustering method using the RapidMiner platform. The stroke dataset was loaded from an Excel file and prepared through a preprocessing stage, including normalization of numerical attributes and conversion of categorical attributes into numerical form to ensure accurate distance calculations in the K-Means algorithm. The K-Means algorithm was then applied to form several clusters based on similarities in patients’ clinical and demographic data. The quality of the clustering results was evaluated using the Davies–Bouldin Index to measure cluster separation and intra-cluster similarity. The results indicate that the K-Means method is capable of producing a more structured representation of stroke risk patterns and can support data-driven decision-making for stroke prevention and management.
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