Abstract
Early diagnosis of breast cancer is crucial for reducing mortality, and machine learning can support the classification of benign and malignant tumors from cytological image features. This study compares six classification algorithms: logistic regression, k-nearest neighbors (k-NN), Naive Bayes, decision tree, support vector machine (SVM) with an RBF kernel, and random forest, on the Breast Cancer Wisconsin (Diagnostic) dataset, which contains 569 samples and 30 numerical features. Models were evaluated with stratified 10-fold cross-validation using accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC), treating the malignant class as positive. SVM and logistic regression achieved the same highest accuracy of 97.54%. SVM led in recall (96.21%), F1-score (96.68%), and AUC (99.56%), and missed only 8 of 212 malignant cases. The decision tree performed worst, with an accuracy of 92.62%. The differences among the best models were small, so simple linear models remain competitive. These findings suggest that SVM with feature standardization is a strong candidate for a diagnostic decision-support system, although validation on external data is still required.
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