Early Prediction of Diabetes Using Transformer Based Ensemble Models

Diabetes is managed in its early stages by employing transformer-based ensemble models for early diabetes prediction. Overcoming the shortcomings of current methods that offer forecasts for forecasting the early stage of diabetes is the main challenge. The goal is to use an ensemble learning strategy to leverage the capabilities of transformer-based models, such as BERT, RoBERTa, and DistilBERT, in order to construct an early prediction model for diabetes. Diabetes is a major global health concern, and reducing its effects depends on early risk factor identification. In medical diagnosis tasks, machine learning-especially transformer-based models-has demonstrated great promise. In this case, an ensemble of transformer models is utilized, and the research focuses on a dataset with a heterogeneous patient population. These provide the most accurate early diabetes prediction and estimates a 97% accuracy rate.