Divisive Hierarchical Clustering towards Identifying Clinically Significant Pre-Diabetes Subpopulations

Type 2 Diabetes Mellitus is a progressive disease with increased risk of developing serious complications. Identifying subpopulations and their relevant risk factors can contribute to the prevention and effective management of diabetes. We use a novel divisive hierarchical clustering technique to identify clinically interesting subpopulations in a large cohort of Olmsted County, MN residents. Our results show that our clustering algorithm successfully identified clinically interesting clusters consisting of patients with higher or lower risk of diabetes than the general population. The proposed algorithm offers fine control over the granularity of the clustering, has the ability to seamlessly discover and incorporate interactions among the risk factors, and can handle non-proportional hazards, as well. It has the potential to significantly impact clinical practice by recognizing patients with specific risk factors who may benefit from an alternative management approach potentially leading to the prevention of diabetes and its complications.