Unleashing the Power of Machine Learning: A Precision Paradigm for Breast Cancer Subtype Classification Using Open-Source Data, with Caution on Dataset Size and Interpretability

This research paper introduces an innovative approach that harnesses the power of machine learning algorithms and open-source data to enhance the precision and personalization of breast cancer treatment decisions. Leveraging the publicly available Breast Cancer Wisconsin (Diagnostic) Dataset. we explore the potential of machine learning in accurately classifying breast cancer subtypes. A wide range of machine learning algorithms, including Logistic Regression, Support Vector Machines, Decision Trees, Random Forest, and Gradient Boosting, are employed to classify breast cancer subtypes. Their performance is evaluated using key metrics such as accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). These metrics provide a comprehensive assessment of the algorithms' effectiveness in subtype classification. The results demonstrate the exceptional performance of the machine learning models, with Logistic Regression emerging as the top-performing algorithm, achieving an accuracy of 99.12%, precision of 98.61 %, recall of 100%, F1-score of 99.3%, and AUC-ROC of 99.87%. These remarkable metrics highlight the potential of machine learning in accurately identifying and distinguishing between malignant and benign breast cancer subtypes. The identified best-performing model holds promise for improving precision and personalized treatment decisions in breast cancer management.

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