Implementation of Machine Learning for Clustering Lethal Diseases Using 21st-Century Global Mortality Data
- Rahmad Hidayat Universitas Sriwijaya, Ogan Ilir, Indonesia
-
Fathoni Fathoni
*
Universitas Sriwijaya, Ogan Ilir,
Indonesia
(*) Corresponding Author
Abstract
Entering the 21st century, the increasing global prevalence of lethal diseases demands a data-driven surveillance system capable of accurately classifying risk levels. Conventional epidemiological monitoring is often insufficient for identifying patterns in the massive volume and complexity of mortality data. This study conducts a comparative analysis between K-Means and Fuzzy C-Means (FCM) algorithms in clustering fatal diseases based on aggregated global death counts using the Global Health Statistics dataset containing 100,000 records. Optimal cluster numbers were determined independently: the Elbow Method yielded K=4 for K-Means, while the Fuzzy Performance Index suggested K=7 for FCM. Performance evaluation using the Silhouette Score under natural configurations showed FCM slightly outperforming K-Means (0.5574 vs. 0.5411), whereas K-Means achieved the highest score under a controlled K=7 setting (0.6014). This research provides a significant contribution by establishing the first direct comparative framework for global multi-disease mortality clustering. The findings indicate that cluster cardinality exerts a stronger influence on clustering quality than the choice of algorithm itself. For policymakers, the results offer a choice between a simple four-tier K-Means risk stratification and the finer granularity of a seven-tier FCM model, providing a scientifically robust, data-driven foundation for evidence-based health intervention and resource allocation strategies.
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