Using Machine Learning to Assess Cardiovascular Risk in Type 2 Diabetes Mellitus Patients without Established Cardiovascular Disease

Background: Cardiovascular (CV) risk assessment is the cornerstone for choosing the most appropriate antihyperglycemic agent. One of the major obstacles in the use of such scores is that the process is time-consuming, which is a deterrent in a busy clinic. The aim of the present analysis was to explore a means of reducing the number of inputs to make the scoring faster and more acceptable to physicians. Materials and Methods: We used the MARK survey dataset to assess the utility of the QRISK 3 CV risk score in the Indian population ( n = 1538); participants were recruited from five zones (east, west, north, south, and central). Machine learning (ML) techniques were used to analyze the existing MARK data. The XGBoost algorithm was used to create the model needed to identify the clinical parameters of interest. Dimension reduction techniques were employed to identify the clinical parameters of importance based on the accrued gain values. As the final step, a confusion matrix was created to assess the model's accuracy and precision. Results: We identified 10 parameters of clinical interest that could predict the 10-year CV risk and were comparable with the QRISK 3 score (with 22 parameters). The parameters identified by ML were age, sex, body mass index, smoking status, blood pressure, treatment with antihypertensive agents, angina, total cholesterol, the total cholesterol/high-density lipoprotein ratio, and chronic kidney disease. This dimension reduction was possible with a model accuracy of 94.4%. Conclusion: There were 10 routinely assessed parameters identified with ML techniques that can assist physicians in determining the CV risk of type 2 diabetes patients within a short period of time.