Application of K-Prototypes Clustermix Algorithm for Clustering Risk Factors of Diabetes Disease
DOI:
https://doi.org/10.33005/jasid.v1i1.9Keywords:
diabetes, mixed data types, clustering, silhouetteAbstract
Diabetes mellitus (DM) is recognized as one of the most rapidly increasing chronic diseases worldwide, posing a significant public health challenge. According to the International Diabetes Federation (IDF), approximately 537 million people were living with diabetes mellitus globally, with projections estimating a rise to 643 million by 2030 and 783 million by 2045. Additionally, the World Health Organization (WHO) reported a 3% increase in mortality rates attributed to diabetes mellitus between 2000 and 2019, underscoring the urgent need for effective risk detection and management strategies. Early identification of risk factors is crucial to mitigating the impact of DM, and clustering analysis offers a promising method for stratifying patients based on risk profiles. This study employs the k-prototypes algorithm, which is particularly suited to clustering datasets with mixed numeric and categorical variables, to analyze DM risk factors. Utilizing data from the 2022 Behavioral Risk Factor Surveillance System (BRFSS) annual survey, the study examines a sample of 2,480 diabetes mellitus patients across the United States. The clustering analysis identified two optimal clusters (k=2) based on a high silhouette score of 0.821, indicating strong cluster cohesion and separation. Cluster 2, consisting of 77 patients, exhibited a higher risk profile for diabetes compared to Cluster 1, which included 2,403 patients. The clusters were characterized by significant differences in average values of key DM risk factors including weight, fruit and vegetable consumption, mental and physical health status, age, alcohol consumption, hypertension, smoking status, physical activity, mobility difficulties, sex, education level, income, and ethnicity. These findings highlight the utility of k-prototypes clustering in identifying high-risk DM subgroups to inform targeted prevention and intervention efforts.
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