Classification of Type 2 Diabetes Using Ensemble Learning Methods

Abstract

Type 2 Diabetes (T2D) is a chronic metabolic disorder that affects millions of people worldwide, making early diagnosis crucial for effective treatment and management. Machine learning (ML) techniques have been increasingly utilized to enhance the classification accuracy of T2D by identifying complex patterns in patient data. This paper introduces a novel approach that leverages ensemble learning methods to improve the classification of T2D. By combining multiple classifiers and focusing on optimal feature selection, the proposed method achieves superior performance compared to traditional classification techniques. The effectiveness of the model was validated using multiple real-world healthcare datasets, demonstrating significant improvements in predictive accuracy.