Predicting Diabetes Using Convolutional Neural Networks and EKG Entropy Analysis

Heart Rate Variability (HRV) is associated with diabetic complications. This analysis can quantify changes in heart rate variability, and it may help detect early alterations in diabetes. This study aimed to design and validate a Convolutional Neural Network (CNN) empowered with entropy metrics (RNC-Rica) for the diagnosis of diabetes through EKG recordings. The RNC-Rica model thus uses CNN architecture for two-dimensional convolution based feature extraction from EKG and simultaneous study of age, HRV measures and entropy measures. From these setups five test setups were evaluated by integrating combinations of convolutional layers and entropy. Through this approach, Test 4 was found to yield the best results (accuracy: 70.3%; sensitivity: 78.4%; specificity: 62.0%; positive predictive value: 68.0%; F1-Score: 72.8%), and the entropy metrics illustrate an improvement in model stability with entropy metrics. The model was capable of early detection in subclinical stages of diabetes. The results indicate that the regularized nearest centroid-Rica model augmented with entropy metrics is an effective tool for the early diagnosis of diabetes via EKG, with high sensitivity, and also statistically significant for clinical classification.