This research work seeks to improve the output of a data mining algorithm that supports diabetic patients' care. The model that is currently in operation uses three variables that are obtained from a general medical appointment database. This work aims to find other characteristics in the database to add them to those already considered to better describe patients to provide more accurate information. The article shows the process followed to improve the results of a k-means grouping algorithm for the follow-up process of diabetic patients. We present the process of defining the considered characteristics that were not part of the model, to analyze and eventually add them. A qualitative comparison between the algorithms is shown and the findings are explained during the analysis of the studied variables, in relation to sex and age of the patients.