Diabetes mellitus is a major chronic metabolic disorder that often leads to serious long-term vascular complications. Traditional monitoring methods focus mainly on metabolic indicators and often miss early vascular changes. This study developed and validated a non-invasive framework for classifying diabetic status based on photoplethysmogram (PPG) pulse morphology. The approach offers a scalable and affordable alternative to invasive blood tests. A dataset from 78 Malaysian participants was analyzed in five phases: signal pre-processing, feature extraction, and statistical ranking. Raw signals were filtered with a 4th-order Chebyshev Type II band-pass filter for accurate waveform analysis. From a wide set of temporal and amplitude features, key biomarkers linked to arterial stiffness and vascular compliance were identified and ranked. Six supervised machine learning models were evaluated: Logistic Regression, Decision Tree (DT), KNN, Support Vector Machine (SVM), Artificial Neural Network (ANN), and Naïve Bayes (NB). ANN and SVM models achieved the highest classification accuracy and AUC. This demonstrates effective distinction between diabetic and non-diabetic status using interpretable waveform features. Validation with a Southeast Asian cohort addresses a demographic gap in the literature. The framework shows that ranked PPG biomarkers can be used for accessible, community-level diabetes screening, especially in healthcare settings with limited resources.
Nayan et al. (Thu,) studied this question.