A hybrid genetic algorithm for the selection of the critical features for risk prediction of cardiovascular complications in Type 2 Diabetes patients

Abstract

The purpose of this study is to present a hybrid approach based on the combined use of a genetic algorithm (GA) and a nearest neighbours classifier for the selection of the critical clinical features which are strongly related with the incidence of fatal and non fatal Cardiovascular Disease (CVD) in patients with Type 2 Diabetes Mellitus (T2DM). For the development and the evaluation of the proposed algorithm, data from the medical records of 560 patients with T2DM are used. The best subsets of features proposed by the implemented algorithm include the most common risk factors, such as age at diagnosis, duration of diagnosed diabetes, glycosylated haemoglobin (HbA1c), cholesterol concentration, and smoking habit, but also factors related to the presence of other diabetes complications and the use of antihypertensive and diabetes treatment drugs (i.e. proteinuria, calcium antagonists, b-blockers, diguanides and insulin). The obtained results demonstrate that the best performance was achieved when the weighted k-nearest neighbours classifier was applied to the CVD dataset with the best subset of features selected by the GA, which resulted in high levels of accuracy (0.96), sensitivity (0.80) and specificity (0.98).