Predicting adolescent type 2 diabetes using intelligent system

Type 2 diabetes mellitus (T2D) in adolescents is a rising public health concern, yet early detection is challenged by limited and imbalanced datasets. This study compares traditional machine learning (ML) and deep learning (DL) methods for predicting adolescent prediabetes using a dataset of 209 records (11.5% prediabetic). Preprocessing included imputation, feature reduction, normalisation, and SMOTE-based class balancing. Baseline ML models - logistic regression, random forest, support vector machine (SVM), and multi-layer perceptron were evaluated via stratified cross-validation, with SVM achieving the best F1-score (0.955). To address class imbalance, synthetic data were generated using generative adversarial networks (GANs) and Wasserstein GANs (WGANs). A 1D convolutional neural network (CNN) trained on the augmented dataset achieved 98.5% accuracy, 96.0% precision, 97.0% recall, and a 96.5% F1-score on the original test set. Results confirm the value of GAN-based augmentation combined with CNNs for improving prediction under limited data, supporting timely T2D risk identification in adolescents. Unlike previous machine learning studies that relied solely on statistical resampling or small neural models, our approach combines GAN-based data synthesis with a one-dimensional convolutional network, yielding a substantial improvement in predictive power and generalisability under limited data conditions.