Machine Learning Algorithms to Predict Recurrence within 10 Years after Breast Cancer Surgery: A Prospective Cohort Study

SIMPLE SUMMARY: No studies have discussed machine learning algorithms to predict recurrence within 10 years after breast cancer surgery. Artificial neural networks (ANN) model is superior to the other forecasting models in terms of accuracy in predicting recurrence within 10 years after breast cancer surgery. Surgeon volume was the best predictor of recurrence within 10 years after breast cancer surgery, followed by hospital volume and tumor stage. For patients who are candidates for breast cancer surgery or who have already received breast cancer surgery, these important predictors can also be used for education in the expected course of recovery and health outcomes. Integration of the machine learning algorithms applied in this study in other clinical decision-making tools would provide additional data that can be used to improve accuracy in predicting recurrence. ABSTRACT: No studies have discussed machine learning algorithms to predict recurrence within 10 years after breast cancer surgery. This study purposed to compare the accuracy of forecasting models to predict recurrence within 10 years after breast cancer surgery and to identify significant predictors of recurrence. Registry data for breast cancer surgery patients were allocated to a training dataset (n = 798) for model development, a testing dataset (n = 171) for internal validation, and a validating dataset (n = 171) for external validation. Global sensitivity analysis was then performed to evaluate the significance of the selected predictors. Demographic characteristics, clinical characteristics, quality of care, and preoperative quality of life were significantly associated with recurrence within 10 years after breast cancer surgery (p < 0.05). Artificial neural networks had the highest prediction performance indices. Additionally, the surgeon volume was the best predictor of recurrence within 10 years after breast cancer surgery, followed by hospital volume and tumor stage. Accurate recurrence within 10 years prediction by machine learning algorithms may improve precision in managing patients after breast cancer surgery and improve understanding of risk factors for recurrence within 10 years after breast cancer surgery.