Design of a Novel Model for Predicting Type-2 Diabetes: Enhanced Ensemble Learning Incorporating Dimensionality Reduction Techniques

The aim of this research is to explore an innovative model for forecasting type-2 diabetes, accomplished through the integration of collaborative methods and dimensionality reduction techniques to improve diagnostic precision. This innovative method culminates in the advance of the “Improved Ensemble Learning with Dimensionality Reduction’’ (IELDR) model, which combines cooperative methods and dimensionality lessening methods to elevate the accuracy of predicting type-2 diabetes. The procedure employed entails applying the IELDR model for type-2 diabetes calculation. This model influences an autoencoder for feature extraction and adopts a two-level assembling super learner framework to train robust cooperative models. The research methodology encompasses to confirmative the IELDR model with the LS-Diabetes dataset, demonstrating heightened organization precision, and supplementary authentication on the PIMA diabetes dataset and Diabetes-2019 dataset to establish the effectiveness of the method in early diagnosis. The research outcome offerings the IELDR model, which realized superior precision and minimalized error rates in forecasting type-2 diabetes, showcasing improved performance associated to other classifiers and feature collection procedures and emphasizing its efficiency in early diagnosis. The practical implication of this study lies in the making of the IELDR model, contribution an effective means to forecast the risk of type 2 diabetes progress in healthy individuals based on their current lifestyle patterns. However, the study’s opportunity is constrained by a relatively inadequate sample size of type-2 diabetes patients undergoing stress, representing the necessity for more widespread research to distinguish dissimilarities in stress levels.