A hybrid machine learning approach combining k-nearest neighbors (kNN) and decision tree (DT) algorithms was proposed to enhance diabetes prediction using the PIMA Indian diabetes dataset. The hybrid model leverages kNN’s ability to capture non-linear patterns and DT’s interpretability and feature selection capabilities, mitigating their individual limitations. The dataset was pre-processed by handling missing values, standardizing features, and addressing class imbalance using the synthetic minority oversampling Technique (SMOTE). The predictions of kNN and DT were integrated through a weighted voting mechanism based on their validation performance. The hybrid model was evaluated using accuracy, precision, recall, and F1-score metrics, demonstrating improved predictive accuracy and generalization. Performance analysis across distance algorithms revealed Chebyshev as the best-performing metric, achieving over 96% accuracy and excelling in recall and F1-score. This study highlights the potential of hybrid machine learning approaches in healthcare, providing scalable and interpretable solutions for complex datasets like diabetes prediction.

Diabetes prediction, Hybrid machine learning, K-nearest neighbors, Decision tree, PIMA Indian diabetes dataset.,test

Kumari P, Gautam S.Hybrid machine learning approach for performance estimation on diabetes dataset. ACCENTS Transactions on Image Processing and Computer Vision. 2024;10(29):20-25. DOI:10.19101/TIPCV.2024.1026005