Comparison of KNN and SVM Algorithms Performance Using SMOTE to Classify Diabetes
Abstract
Diabetes frequently goes undetected or is diagnosed too late. Consequently, it may lead to a range of serious complications, such as organ damage, stroke, and heart disease. The International Diabetes Federation (IDF) reports that 10.5% of the adult population aged 20 to 79 are diagnosed with diabetes, and almost half are unaware of the condition. Hence, the number of people with diabetes has increased by fourfold compared to the prior period. One essential step for preventing complications in patients with diabetes is early detection, one of which is by utilizing artificial intelligence (AI) technology, namely data mining. Therefore, knowledge about effective algorithms used to detect diabetes is needed. This study aimed to compare two algorithms, namely k-nearest neighbor (KNN) and support vector machine (SVM), for diabetes classification using the synthetic minority oversampling technique (SMOTE). In this study, both algorithm performance was measured using the machine learning life cycle method. The results showed they had good performance in detecting diabetes; yet, there were significant performance differences between the two. The SVM algorithm with radial basis function (RBF) kernel achieved 81.67% accuracy, 85.91% precision, 79.01% recall, and 82.32% F1 score. Meanwhile, the KNN algorithm with k = 3 found through cross-validation achieved 83.33% accuracy, 85.00% precision, 83.95% recall, and 84.47% F1 score. Based on confusion matrix evaluation, KNN showed superior performance compared to SVM in terms of accuracy and other evaluation metrics. These results indicate that KNN is more effective in detecting diabetes in the dataset used in this study.
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