Association rule mining of time-based patterns in diabetes-related comorbidities on imbalanced data: a pre- and post-diagnosis study.

Abstract

Type 2 diabetes mellitus (T2DM) is affecting over 529 million adults and anticipated to impact 1.3 billion by 2050. This disease often coexists with multiple comorbidities, which can complicate its management. These comorbidities not only increase morbidity and mortality but also challenge the effectiveness of interventions designed to manage diabetes and improve patient outcomes. We analysed imbalanced data of 25.065 patients deriving from the Clinical Centre of the University of Debrecen, Hungary. The aim of the study was to explore the prevalence and temporal patterns of comorbidities before and after the diagnosis of T2DM using Association Rule Mining (ARM) and network visualization. The initial five years following T2DM diagnosis mark a spike in newly emerging health conditions. Hypertension frequently occurs at an earlier stage, while pneumonia, eye-related disorders, and ischemic heart disease consistently appear throughout the progression of the disease. The ARM analysis showed that both acute and chronic kidney diseases, as well as respiratory disorders are common after T2DM diagnosis. Certain gender-specific trends, such as higher instances of heart failure and acute kidney injury in males, are also notable. The study highlights how ARM techniques reveal complex patterns in chronic disease management, suggesting potential pathways for targeted treatments.