Breast cancer is a significant global health problem, and HER2-positive breast cancer accounts for a substantial proportion of cases. The combination of Trastuzumab and Pertuzumab monoclonal antibodies with chemotherapy has demonstrated effectiveness in achieving pathologic complete response (pCR) among HER2-positive breast cancer patients. This study aims to develop an interpretable machine learning model to predict pCR in patients undergoing this neoadjuvant treatment. Previous studies have explored predictors of pCR and utilized statistical techniques, but no prior research has applied machine learning to this specific treatment. This work proposes a rule-based interpretable method based on Dempster-Shafer theory. The model is trained using a dataset of 390 patients, with 57% achieving pCR. The performance of the model is compared with other classification algorithms, demonstrating its moderate but promising results. This work highlights the importance of combining accuracy and interpretability in healthcare applications, providing insights into the factors influencing treatment response in HER2-positive breast cancer patients.

Breast Cancer, Pathologic Complete Response, Interpretability, Supervised Learning, Expert Systems, Dempster-Shafer Theory