An Explainable and Computationally Efficient Machine Learning Framework for Breast Cancer Diagnosis Using Predictive and Interpretability Analysis

Abstract

Accurate breast cancer diagnosis is important for supporting early detection and improving clinical decision-making.. This study examines the theoretical and interpretative principles that support the machine learning algorithms applied to the Wisconsin Breast Cancer dataset and evaluates their ability to differentiate between benign and malignant neoplasms. To improve reliability, five-fold stratified cross-validation was additionally performed, and average performance values were considered. The logistic regression model achieved a high ROC-AUC value of 0.9960, whereas the performance of the random forest classifier was almost similar, with a slightly lower score. To enhance methodological transparency and interpretability, this study employs explainable AI techniques, including permutation importance, SHAP values, local and global explanation metrics, and partial dependence plots. These methods provide valuable insights into the extent to which tumour size and concavity contribute to malignancy prediction. A systematic study of computational complexity has been conducted, and the results show that the training and inference latency of logistic regression is significantly lower than that of random forests, indicating its potential suitability for future resource-constrained clinical decision-support investigations after external validation. The proposed framework achieved high discrimination performance while maintaining interpretability and low computational cost, suggesting its potential suitability for future clinical decision-support investigations after external validation.