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

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.

Keywords: 10-year survival; artificial neural network; breast cancer surgery; machine learning.

Figure 1

Flowchart of the study procedure.

Figure 2

Receiver operating characteristics (ROC) curve for machine learning models in predicting recurrence within 10 years after breast cancer surgery in (A) training dataset and (B) testing dataset. (A) The comparison of the ROC curve between the forecasting models (artificial neural network (ANN), K nearest neighbor (KNN), support vector machine (SVM), naive Bayes classifier (NBC), and Cox regression (COX)) models to predict recurrence within 10 years after breast cancer surgery in the training dataset (n = 798). (B) The comparison of the ROC curve between the forecasting models to predict recurrence within 10 years after breast cancer surgery in the testing dataset (n = 171).