Enhancing Diabetes Prediction Accuracy through Hybrid Machine Learning Models: A Comparative Study

Abstract

This study investigates the effectiveness of various machine learning (ML) models in predicting the onset of diabetes, emphasizing the superior performance of hybrid models over single learner models. Employing a dataset comprising 10,000 individuals with features like Glucose level, BMI, Insulin, and more, we meticulously processed and engineered the data to optimize it for ML applications. We developed several models, including Decision Trees, Random Forest, KNN, and XGBoost, and then advanced to hybrid models using ensemble techniques like stacking and soft voting classifiers. Our findings indicate that hybrid models significantly outperform single learner models. These models achieved remarkable accuracy (98.11%), precision (97.31%), and ROC AUC (99.82%), highlighting their potential in clinical settings. The study underscores the value of hybrid ML models in enhancing predictive accuracy and reliability in diabetes diagnostics.

Keywords

• Hybrid Model
• Decision Trees
• Random Forest
• XGBoost
• Diabetes