A Clustering-Based Approach to Analyse Examinations for Diabetic Patients

Health care data collections are usually characterized by an inherent sparseness due to a large cardinality of patient records and a variety of medical treatments usually adopted for a given pathology. Innovative data analytics approaches are needed to effectively extract interesting knowledge from these large collections. This paper presents an explorative data mining approach, based on a density-based clustering algorithm, to identify the examinations commonly followed by patients with a given disease. To cluster patients undergoing similar medical treatments and sharing common patient profiles (i.e., patient age and gender) a novel combined distance measure has been proposed. Furthermore, to focus on different dataset portions and locally identify groups of patients, the clustering algorithm has been exploited in a multiple-level fashion. Based on this cluster set, a classification model has been created to characterize the content of clusters and measure the effectiveness of the clustering process. The experiments, performed on a real diabetic patient dataset, demonstrate the effectiveness of the proposed approach in discovering interesting groups of patients with a similar examination history and with increasing disease severity.