Identifying High-Risk Patients for Diabetes using Machine Learning

Diabetes is a chronic disease that represents a major challenge for global health. However, current technological advances allow us to monitor blood glucose levels in real time throughout the day, enabling better monitoring and faster intervention when needed. In this work, we aim to improve current methods of classifying patients at risk of diabetes using glycated hemoglobin test (HbA1c). The eleven features extracted from synthetic continuous glucose monitoring (CGM) data were used with two classifiers, namely decision tree (DT) and Random Forest (RF), to classify patients into four categories: healthy, moderate pre-diabetic, severe pre-diabetic and diabetic. The results obtained are promising, with an overall accuracy of around 92%.