Interpretable machine learning for personalized breast cancer screening recommendations.
Authors
Affiliations (3)
Affiliations (3)
- Department of Mechanical, Industrial and Mechatronics Engineering, Toronto Metropolitan University, 350 Victoria Street, Toronto, ON, M5B 2K3, Canada.
- DeGroote School of Business, McMaster University, 1280 Main Street West, Hamilton, ON, L8S 4M4, Canada. [email protected].
- Grado Department of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, 1145 Perry Street, Blacksburg, VA, 24061, USA.
Abstract
Breast cancer is the most common non-skin cancer and the second leading cause of cancer death in U.S. women. Early detection and timely intervention are thus critical in reducing breast cancer-related deaths. Existing literature for the design of personalized mammography screening is mainly concerned with modeling the problem as a partially observable Markov decision process, which are computationally difficult to solve. In this study, we propose a machine learning-based approach for identifying the personalized screening recommendations using medical history and associated risk factors for individual patients. We find that machine learning models could provide a high degree of accuracy at drastically reduced computational complexity. Furthermore, once trained to sufficient accuracy, we ascertain explainable insights into machine learning model decisions. These insights yield a set of actionable decision rules that healthcare providers could use to support informed patient screening decisions. Overall, our study showcases the potential of machine learning in providing accurate and actionable recommendations for breast cancer screening.