Breast Cancer Classification using Machine Learning : A Review

Breast cancer represents a significant global health issue that profoundly affects women, emphasizing the critical importance of early detection for successful treatment. Machine learning algorithms have demonstrated remarkable potential in accurately categorizing different subtypes of breast cancer. This review paper focuses on providing a comprehensive summary of breast cancer classification using machine learning techniques. We delve into an examination of diverse machine learning algorithms, including Support Vector Machine (SVM), Decision Tree, Naive Bayes (NB), and K Nearest Neighbors (k-NN), for the purpose of breast cancer classification. Their performance is thoroughly compared and evaluated. Furthermore, we discuss the challenges and future directions associated with machine learning-based breast cancer classification. Overall, this review paper serves as an invaluable resource for researchers in the field by presenting a comprehensive overview of the current state-of-the-art in breast cancer classification using machine learning. Our objective is to explore different methodologies to effectively and efficiently detect breast cancer early using machine learning. Recognizing the critical significance of early cancer discovery, we employ various machine learning algorithms to predict whether a tumor is benign (noncancerous) or malignant (cancerous) based on the provided data features.