Performance Comparison of Breast Cancer Classification Methods: Naive Bayes vs. Support Vector Machine
Abstract
Breast cancer is a global health issue where early detection and accurate diagnosis play a key role in improving patients' chances of successful recovery. Despite their widespread use and proven effectiveness, traditional diagnostic methods have limitations that have prompted the development of computational approaches. Machine learning is one such approach. Numerous prior studies have investigated various algorithms, including Naive Bayes and Support Vector Machine (SVM), for breast cancer classification; however, research directly comparing their performance on the same dataset is still limited. This study evaluates the efficacy of Naive Bayes and SVM methods for classifying breast cancer diagnoses as benign or malignant using the publicly available Wisconsin Diagnostic Breast Cancer (WDBC) dataset. The research stages include data collection, preprocessing, splitting the dataset into training and test sets at 70% to 30%, standardizing features for the SVM model, applying both algorithms, and evaluating performance using metrics such as accuracy, precision, recall, and F1-score. The test results indicate that the SVM algorithm achieved an accuracy of 98.25%, precision of 100%, recall of 95%, F1-score of 98%, and MCC of 0.96. Conversely, the Naive Bayes algorithm achieved 94.15% accuracy, 94% precision, 91% recall, a 93% F1-score, and 0.88 MCC. The comparison results indicate that SVM outperforms Naive Bayes on this dataset, especially in reducing false- positive and false-negative rates. This research is expected to serve as a valuable resource for medical professionals and researchers seeking to select the appropriate machine learning algorithm for early breast cancer detection.
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