Machine Learning-based Breast Cancer Classification Using Logistic Regression and Random Forest

Abstract Breast cancer categorization is vital for prompt identification and strategic therapy planning. In this research, a machine learning model was created utilizing the Breast Cancer Wisconsin (Diagnostic) dataset to categorize tumors as malignant or benign. The model achieved an overall accuracy of 97.36%, demonstrating strong predictive performance.For the malignant class, the model attained a precision of 0.976, recall of 0.953, and F1-score of 0.965, indicating a strong accuracy in detecting cancerous instances. For the benign class, the model achieved a precision of 0.972, recall of 0.986, and F1-score of 0.979, confirming its effectiveness in correctly classifying non-cancerous cases. The macro average F1-score was 0.972, and the weighted average F1-score was 0.974, further emphasizing equitable performance in both categories.These findings indicate that the suggested classification method offers a reliable and precise diagnostic instrument, with possible uses in clinical decision support systems for breast cancer detection.