Machine Learning-Based Diabetes Prediction Using Ensemble Methods

Abstract

Diabetes mellitus is a chronic metabolic disorder that requires early detection to prevent severe complications and improve patient outcomes. This paper presents a Machine Learning-Based Diabetes Prediction framework using ensemble methods to enhance predictive performance and reliability. The proposed system utilizes clinical datasets such as the Pima Indians Diabetes dataset and applies preprocessing techniques including data normalization, missing value handling, and feature selection to improve data quality. Multiple machine learning algorithms, including Decision Trees, Support Vector Machines, and Logistic Regression, are combined using ensemble strategies such as bagging, boosting, and stacking to achieve improved accuracy. The framework leverages the strengths of individual models while reducing variance and bias. Experimental results demonstrate that the ensemble-based approach outperforms standalone models in terms of accuracy, precision, and recall. Additionally, integration with prior research in healthcare analytics and hybrid learning enhances model robustness and generalization. The study highlights the effectiveness of ensemble learning techniques in developing accurate and scalable diabetes prediction systems suitable for real-world healthcare applications.