Generative AI models for type 2 diabetes mellitus risk prediction.

Diabetes, precisely Type II Diabetes Mellitus (T2DM), is a prevalent global chronic condition. This study focuses on improving the accuracy of predicting T2DM onset and risk by utilizing Generative Artificial Intelligence (GenAI) based synthetic data generation and innovative feature selection techniques. GenAI models such as Deep Tabular Augmentation (DTA) and Large Language Models (LLM) were utilized to address class imbalance and data scarcity of diabetes class for prediction. The Representative Instances-based Fuzzy Rough Set Feature Selection (FRS-RI) method was employed for optimal feature selection. Three diabetes datasets - Sylhet, Obesity, and Diagnostic Features - were employed. After FRS-RI feature selection and synthetic data generation, Machine Learning (ML), Ensemble Learning (EL), and Deep Learning (DL) models were trained on these datasets. The ML, EL, and DL models achieved impressive accuracy, precision, and recall scores: 95.19%, 0.96, and 0.94 for the Sylhet Dataset; 100%, 1.00, and 1.00 for the Obesity dataset; and 97.44%, 0.97, and 0.94 for the NIDDK-DF Dataset. The model's ability to generalize to new diabetic data was demonstrated by enhanced test accuracies of 98.37% and 97.33% obtained when the suggested techniques were applied to benchmark datasets such as PIMA and LMCH, respectively. Emphasis was also placed on model explainability to justify predictions for clinical presentation.