Review of Machine Learning Algorithms for Breast Cancer Diagnosis

Abstract

Breast cancer remains a global health challenge, contributing significantly to mortality worldwide. Addressing this critical issue, we review existing machine-learning algorithms for accurate breast cancer diagnosis. Our research employs an ensemble of algorithms, including Gaussian Naive Bayes, XGBoost, Support Vector Machine, Logistic Regression, Principal Component Analysis, Linear Discriminant Analysis, k Nearest Neighbors, Random Forest, Decision Tree Classifier, and an ensemble classifier. This study relies on specialized data preprocessing and balancing techniques, ensuring the reliability of the analysis. Our approach utilizes two prominent datasets, the Wisconsin Breast Cancer Diagnosis (WBCD) and the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, carefully partitioned with a 5-fold cross-validation strategy. The evaluation protocol is comprehensive, spanning diverse performance metrics. We explore confusion matrices, accuracy, precision, recall, F1 score, the area under the curve (AUC), and Receiver Operating Characteristic (ROC) curves. Notably, in classifying benign and malignant tumors using the WBCD dataset, the SVM model stands out with a detection accuracy of 99.27%, complemented by precision, recall, and F1-score, all achieving 99.28%. Transitioning to the WDBC dataset, the XGBoost model emerges as the optimal choice, attaining accuracy, precision, recall, and F1-score values of 98.25%.