Impact of Different Kernels on Breast Cancer Severity Prediction Using Support Vector Machine - Kunti Mahmudah; Sugiyarto Surono; Rusmining Rusmining; Fatma Indriani (Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA, 2026)
Breast cancer poses a critical global health challenge and continues to be one of the most prevalent causes of cancer-related deaths among women worldwide. Accurate and early classification of cancer severity is essential for improving treatment outcomes and guiding clinical decision-making, since timely intervention can significantly reduce mortality rates and enhance patient survival. This study evaluates the performance of Support Vector Machine (SVM) models using different kernel functions of Linear, Polynomial, Radial Basis Function (RBF), and Sigmoid for breast cancer severity prediction. The impact of feature selection was also examined, using the Random Forest algorithm to select the top features based on Mean Decrease Accuracy (MDA), which serves to reduce redundancy, improve interpretability, and enhance model efficiency. Experimental results show that the RBF kernel consistently outperformed other kernels, especially in terms of sensitivity, a critical metric in medical diagnostics that emphasizes the ability of the model to identify positive cases correctly. Without feature selection, the RBF kernel achieved an accuracy of 0.9744, a sensitivity of 0.9772, a precision of 0.9722, and an AUC of 0.9968, indicating strong performance across all evaluation metrics. After applying feature selection, the RBF kernel further improved the accuracy to 0.9754, the sensitivity to 0.9770, the precision to 0.9742, and the AUC to 0.9975, which demonstrated enhanced generalization and reduced overfitting, highlighting the benefits of targeted feature reduction. While the Polynomial kernel yielded the highest precision (up to 0.9799), its lower sensitivity (as low as 0.9237) indicates a greater risk of false negatives, which is particularly concerning in cancer detection. These findings underscore the importance of optimizing both kernel function and feature selection. The RBF kernel, when combined with targeted feature selection, offers the most balanced and sensitive model, making it highly suitable for breast cancer classification tasks where diagnostic accuracy is vital