Abstract

<p>The advent of machine learning and artificial intelligence has revolutionized the field of medical diagnostics, particularly in the <br>early detection of breast cancer. This study presents the development and evaluation of a high-accuracy breast cancer detection <br>model using the Wisconsin Breast Cancer dataset (WBCD). The model's performance was meticulously analyzed, focusing on its <br>ability to accurately distinguish between benign and malignant cases. The results demonstrated exceptional precision, recall, <br>and F1 scores of 0.988 for both classes, indicating a balanced performance with minimal false positives and false negatives. The <br>confusion matrix further highlighted the model's robustness, with a true positive and true negative rate of 494 and a minimal <br>misclassification rate. The training progress, as depicted by the epoch plot, showed a steady increase in accuracy from 70% to <br>nearly 99%, indicating effective learning and generalization capabilities. This study's findings underscore the potential of the <br>developed model for early detection and timely intervention in breast cancer, offering a promising tool for clinical practice. The <br>implications of these results are significant, suggesting a future where AI-driven diagnostics can substantially improve patient <br>outcomes and reduce the burden of breast cancer.</p>