Comparative Analysis of Machine Learning Algorithms for Breast Cancer Classification

Abstract

Breast cancer is one the most fatal diseases among women. Therefore, the need to develop reliable diagnostic tools to early detect breast cancer for treatment. Machine Learning is the powerful approach to breast cancer classification. This systematic review aims to provide a comprehensive comparison of various machine learning algorithms used to the classification of breast cancer. The key algorithms, including support vector machines (SVM), decision tree (DT), random forest (RF), K-nearest neighbor (KNN), Logistic Regression (LR) and Convolutional neural network (CNN), evaluate the performance of the metrics and the accuracy of the model. We analyze the numerous data from the various papers, and then find the strengths and the limitations of each algorithm in the different scenarios. In addition, we discuss the impact of the data preprocessing techniques, selection methods and the role of ensemble learning in the classification performance. We found that no single algorithm consistently outperforms others across all metrics, suggesting a hybrid approach may offer the most robust solution.