Data-driven cardiovascular risk prediction and prognosis factor identification in diabetic patients

Abstract

The increase of patients diagnosed with non-communicable diseases (NCDs) has reached high levels, becoming an important global health issue. NCDs are the cause of decease of 41 million people yearly, accounting for 71% of all deaths world-wide. Among NCDs, cardiovascular diseases (CVDs) present an increasing prevalence, leading to severe complications and death. Patients with Type 1 diabetes are more prone to develop CVD events, and refer to greater mortality rates than the general population. An early risk prediction of developing CVD events in T1D patients could support clinicians in adequate interventions, including lifestyle changes or pharmacological and surgical treatments. In this work, we use feature selection techniques and data-driven models to identify relevant prognostic factors associated with the 10-year CVD risk, designing models for its earlier prediction. Demographic and clinical variables related to the patients' lifestyle were considered, including the interpretation of the variables' impact on the prediction models. Experimental results showed that linear data-driven models are best for CVD prediction, outperforming results of other techniques. Regarding the risk factors, the age was the most important variable for predicting CVD, being present in all the analyzed models. This work showed to be promising for predicting CVD, identifying risk factors, and paving the way for clinical decision-making.