Probability-Based Correction Methodology for Breast Cancer Prediction

This research article presents a probability-based correction methodology designed to enhance the accuracy of computer-aided breast cancer prediction. It addresses the challenges faced by existing machine learning classifiers, which can generate false negatives and false positives, particularly in clinical settings. The proposed approach integrates a probabilistic decision layer based on Optimal Stopping Theory (OST) to improve the reliability of predictions. The methodology was applied to the Wisconsin Breast Cancer Diagnostic dataset, achieving perfect accuracy and demonstrating superior performance in precision, recall, and F1-score during rigorous cross-validation. The study evaluates five machine learning classifiers—Bagging, K-Nearest Neighbors, AdaBoost, Gradient Boosting, and Multilayer Perceptron—highlighting the effectiveness of the correction technique in mitigating inconsistencies and enhancing clinical applicability. The findings suggest that this framework not only improves breast cancer diagnosis but also has potential applications in other medical diagnostics, offering a scalable solution for high-stakes classification tasks.