Detection and Classification of Breast Cancer Using Different Machine Learning Classifier

Navigating the complex landscape of breast cancer diagnostics, this study addresses the critical challenge of early detection and classification. Breast cancer remains a significant health concern, underscoring the urgency for advanced method-ologies. Employing various machine learning classifiers kMPN (k-Most Proximate Neighbor), Naive Bayes, and the formidable Support Vector Machine (SVM)-we rigorously assessed their capabilities in discriminating between benign and malignant tumors. In a groundbreaking revelation, SVM emerged as the indisputable leader, achieving an exceptional accuracy of 93.72%. This milestone not only represents a pivotal advancement in breast cancer diagnostics but also underscores the indispensable role of machine learning in healthcare. As we grapple with the intricacies of breast cancer, SVM's outstanding accuracy positions it as the optimal choice for refining and advancing detection methodologies, offering a beacon of hope for improved patient outcomes and transformative progress in addressing this pervasive health issue.