To design the proposed method, information concerning social scope and self-care of 153 diabetic patients was presented to the K-means clustering algorithm, which divided the data into two groups: high risk and low risk of developing diabetic foot. 
K-means clustering algorithm
In the operational stage, the Euclidian distance from the information vector to the centroids of each group of risk is used as criterion for classification. 
Euclidian distance