Performance Comparison of K-Nearest Neighbor, Decision Tree, and Support Vector Machine Algorithms for Diabetes Classification
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Abstract
This paper investigates the performance of three supervised machine learning algorithms K-Nearest Neighbor (KNN), Decision Tree (DT), and Support Vector Machine (SVM) for diabetes classification using the Pima Indians Diabetes Dataset. The study aims to provide a fair and consistent comparison by applying unified preprocessing procedures, including median imputation for clinically invalid values, feature standardization, and stratified 5-fold cross-validation. Model performance is evaluated using accuracy, precision, recall, and F1-score, with particular emphasis on recall for the diabetic class due to its clinical significance in reducing false negative diagnoses. Experimental results show that the Decision Tree model achieves the most balanced performance, with an average accuracy of 0.78 and an F1-score of 0.75, while maintaining higher recall for diabetic cases compared to KNN and SVM. Although SVM and KNN demonstrate acceptable overall accuracy, both models exhibit limitations in identifying minority-class instances. These findings highlight the importance of algorithm selection based not only on accuracy but also on clinical priorities such as interpretability and sensitivity to positive cases. The study contributes practical insights for the development of reliable machine learning–based decision support systems for early diabetes screening.
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