Diabetes Prediction Using Stacking Ensemble Model

Diabetes is one of the longterm conditions that are becoming common with every passing year. The problems arise when diabetes can't be detected early and dealt with timely. The prediction system for diabetes utilizes the ensemble learning in order to provide accurate and reliable predictions. An ensemble model of stacking ensemble is created by using RF, AB and XGB as base with the final predictor as LR, with Pima data in handling imbalanced data by SMOTE, with hyperparameter tuning for optimization. Confusion matrices such as recall, accuracy, precision, and F-measure can be used for evaluation of the results. This approach supports the early detection of diabetes by providing accurate predictions, therefore permitting timely interventions and personalized healthcare.