Predicting Optimal Chemotherapy Regimens in Breast Cancer Treatment using Machine Learning

Abstract

Breast cancer treatment involves personalized chemotherapy regimens to improve patient outcomes, and selecting the optimal regimen is a crucial challenge. Machine learning (ML) algorithms offer potential solutions by analyzing complex clinical data and predicting effective treatment strategies. This study conducts a comparative analysis of multiple ML algorithms, including decision trees, support vector machines, neural networks, and ensemble methods, to predict the optimal chemotherapy regimens for breast cancer patients. Using a dataset comprising clinical, pathological, and molecular features, the models are trained and evaluated based on accuracy, sensitivity, specificity, and predictive power. The results demonstrate that ensemble methods outperform other approaches, offering higher prediction accuracy and robustness. Feature importance analysis further highlights significant predictive factors for chemotherapy response. This comparative study provides valuable insights into the strengths and limitations of various ML algorithms in the context of personalized chemotherapy for breast cancer, contributing to more informed decision-making and potentially enhancing treatment efficacy.