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Neural Network-based Diabetic Type II High-Risk Prediction using Photoplethysmogram Waveform Analysis

This work aims to predict and classify patients into diabetic and nondiabetic subjects based on age and four independent variables extracted from the analysis of photoplethysmogram (PPG) morphology in time domain. The study has two main stages, the first one was the analysis of PPG waveform to extract b/a, RI, DiP, and SPt indices. These parameters contribute by some means to the prediction of diabetes. They were statistically significant and correlated with the HbA1C test. The second stage was building a neural network based classifier to predict diabetes. The model showed an accuracy of 90.2% in training phase and an accuracy of 85.5% in testing phase. The findings of this research work may contribute towards the prediction of diabetes in early stages. Also, the proposed classifier showed a high accuracy in predicting the existence of diabetes in Saudi people population.