Effective diabetes management requires precise non-invasive monitoring systems that surpass the limitations of traditional glucose testing. Based on photoplethysmography (PPG) signals, this study presents a novel Artificial Intelligence (AI) driven framework for non-invasive blood glucose monitoring (NIBGM) using machine learning, deep learning, and neural network on four diabetic classes (hyperglycaemia, hypoglycaemia, pre-diabetic, and normal) that are further validated by five statistical techniques (10 K-fold, bootstrap P-value, sensitivity vs false negative, brier-score, heatmap). The data is pre-processed and mapped with continuous glucose monitoring (CGM) readings, further balanced by AI-based Synthetic minority oversampling technique (SMOTE) analysis. The 12 features (morphological, statistical, temporal, and spectral) are scrutinised using a set of four statistical methods (L1 regularization, cross validation, hyper parameter tuning, and recursive method). This research also reports a set of latest six classifiers in conjunction, to assess the AI-based accuracy for the first time on diabetic date set. The classifiers include: Ensemble (bilstm + lightgbm), catboost, gradient boosting models, convolution neural networks, random forest. The ensemble and Lightgbm provided highest of 89% accuracy and 0.889 weighted F1 score. The models showed flawless sensitivity for all four classes. The obtained results are validated by a set of five abovementioned statistical techniques concluding an extensive layer of validation, not reported yet. The results conclude the clinical potential of framework to provide accessible, accurate, and thus dependable diabetes monitoring smart healthcare AI technologies.
Bilal et al. (2025) studied this question.