Clustering Analysis of Ankle-Brachial Index Related Metabolic and Body Composition Profiles Using K-Means Approach.

Abstract

Purpose: The Ankle-brachial Index (ABI) is a non-invasive diagnostic tool used to assess peripheral perfusion disorders. However, the increasing number of clinical parameters involved in ABI assessment may complicate clinical decision-making. This study aimed to explore clustering of metabolic and body composition profiles in relation to ankle-brachial index (ABI) values using a K-means approach to identify patterns associated with peripheral perfusion.

Patients and methods: A cross-sectional study was conducted using primary and secondary data from 1093 participants collected from community health centres, hospitals, and academic institutions in the Malang region between 2023 and 2025. Thirteen clinical and metabolic parameters were analysed, including age, blood pressure, blood glucose, lipid profile, uric acid, and body composition indices. ABI was measured using vascular Doppler ultrasound, and body composition was assessed using bioelectrical impedance analysis. K-means clustering was performed using the K-means algorithm, both with and without feature selection based on the gain ratio method. Cluster quality was evaluated using the Elbow Method and Silhouette Score.

Results: The optimal clustering solution was identified at k = 2 for both approaches. Feature selection reduced the number of parameters from 13 to 7 and improved cluster quality, with the Silhouette Score increasing from 0.503 to 0.559. Visceral fat, resting metabolism, body mass index, body fat, skeletal muscle, subcutaneous fat, and cell age were the most influential parameters in distinguishing patient clusters. The resulting clusters represented distinct metabolic risk profiles, ranging from healthier individuals to those with elevated cardiometabolic risk.

Conclusion: K-means clustering identifies distinct metabolic and body composition profiles associated with ankle-brachial index (ABI) values. These clusters represent data-driven patterns and should be interpreted as descriptive associations rather than diagnostic or predictive categories. The findings provide exploratory insights into metabolic characteristics related to peripheral perfusion and suggest that metabolic and body composition parameters may help group ABI into distinct profiles. This approach may have potential relevance in nursing practice by supporting clinical consideration; however, further validation is required.