A Machine Learning Framework for Breast Cancer Treatment Classification Using a Novel Dataset

TL;DR

This study develops machine learning models using a novel breast cancer dataset to predict treatment options, demonstrating high accuracy and interpretability, thus supporting personalized treatment decisions.

Contribution

Introduces a machine learning framework with novel dataset application for predicting breast cancer treatment, emphasizing model interpretability and performance evaluation.

Findings

Gradient Boosting Machine achieved 77.18% accuracy

XGBoost and AdaBoost also showed strong performance

Model interpretability was enhanced using SHAP values

Abstract

Breast cancer (BC) remains a significant global health challenge, with personalized treatment selection complicated by the disease's molecular and clinical heterogeneity. BC treatment decisions rely on various patient-specific clinical factors, and machine learning (ML) offers a powerful approach to predicting treatment outcomes. This study utilizes The Cancer Genome Atlas (TCGA) breast cancer clinical dataset to develop ML models for predicting the likelihood of undergoing chemotherapy or hormonal therapy. The models are trained using five-fold cross-validation and evaluated through performance metrics, including accuracy, precision, recall, specificity, sensitivity, F1-score, and area under the receiver operating characteristic curve (AUROC). Model uncertainty is assessed using bootstrap techniques, while SHAP values enhance interpretability by identifying key predictors. Among the tested models, the Gradient Boosting Machine (GBM) achieves the highest stable performance (accuracy = 0.7718, AUROC = 0.8252), followed by Extreme Gradient Boosting (XGBoost) (accuracy = 0.7557, AUROC = 0.8044) and Adaptive Boosting (AdaBoost) (accuracy = 0.7552, AUROC = 0.8016). These findings underscore the potential of ML in supporting personalized breast cancer treatment decisions through data-driven insights.