An Empirical Comparison of Machine Learning Algorithms for Breast Cancer Detection

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Abstract
Breast cancer continues to be a major global health challenge, necessitating the development of accurate and reliable diagnostic systems. This study presents a comparative evaluation of multiple machine learning classification models aimed at enhancing breast cancer detection. Three feature selection techniques which are Principal Component Analysis (PCA), Pearson Correlation Coefficient (PCC), and Backpropagation Neural Networks (BNN) were employed to reduce dimensionality and extract relevant features. The performance of six classifiers which are Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Logistic Regression (LR), Decision Tree (DT), Naïve Bayes (NB), and Artificial Neural Network (ANN) was analyzed based on accuracy, precision, recall, specificity and f1-Score. Results show that among the evaluated classifiers, Random Forest and Support Vector Machine (SVM) consistently delivered the highest performance, with Random Forest achieving up to 98.8% accuracy and SVM up to 98.0%, particularly when trained on features selected through Backpropagation Neural Networks (BNN). K-Nearest Neighbors (KNN) and Artificial Neural Network (ANN) also demonstrated strong results, outperforming traditional models like Logistic Regression and Decision Tree in most scenarios. These outcomes underscore the superior classification capabilities of non-linear and ensemble-based models in handling complex feature interactions, affirming their suitability for accurate and robust breast cancer detection.

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