Breast Cancer Prediction Using Shapely and Game Theory in Federated Learning Environment

Abstract

Breast cancer is a critical health issue affecting the well-being of women. Breast cancer is one of the most common causes of the increase in the mortality rate of women around the world. Early detection of breast cancer can, to some extent, decrease the number of deaths caused and also improve treatment outcomes and patient survival rates. Traditional Machine learning (ML) and Deep learning (DL) approaches have proven to be more successful in predicting breast cancer. In terms of privacy and early detection, Federated Learning (FL), a decentralized ML approach, offers a promising solution for training predictive models on distributed healthcare data while ensuring privacy and security. This paper proposes a novel framework that combines the benefits of integrating Shapley values and game theory concepts with FL for breast cancer prediction. The framework uses Shapley values for feature selection from 30 features of the Wisconsin Diagnostic Breast Cancer (WDBC) dataset from University of California Irvine machine learning repository (UCI ML). The framework also addresses the issue of poor-performing clients by introducing a payoff mechanism based on individual client accuracy. Clients with higher accuracy are given greater influence in the model aggregation process, encouraging client competition and improving the overall model performance. Our framework proves to be promising by achieving a prediction accuracy of 94.73% in the FL environment. The proposed approach provides a privacy-preserving solution for breast cancer prediction in an FL environment, by combining Shapley values and game theory. The results of this study can help in the development of more accurate and robust breast cancer prediction models, contributing to improved patient outcomes and healthcare decision-making.

Keywords

• Federated learning
• machine learning
• Shapley values
• game theory
• incentive mechanism