Breast Cancer Surgery 10-Year Survival Prediction by Machine Learning: A Large Prospective Cohort Study

SIMPLE SUMMARY: This study provided an analysis of machine-learning algorithms and the ability to predict 10-year survival after breast cancer surgery. The univariate analyses and the global sensitivity analysis provided in this study are especially helpful. This represents a novel opportunity for understanding the significance of a preoperative SF-36 PCS score, a preoperative SF-36 MCS score, postoperative recurrence, and tumor stage in predicting 10-year survival after breast cancer surgery and could lead to clinicians being better informed about the precision and efficacy of management for these patients. These results encourage a broader international validation of language models in clinical practice and emphasize that preoperative physical and mental functioning should always be an integral part of cancer care. Future studies may investigate further refinements of the machine-learning algorithms applied in this study and their potential for integration with other clinical decision-making tools. ABSTRACT: Machine learning algorithms have proven to be effective for predicting survival after surgery, but their use for predicting 10-year survival after breast cancer surgery has not yet been discussed. This study compares the accuracy of predicting 10-year survival after breast cancer surgery in the following five models: a deep neural network (DNN), K nearest neighbor (KNN), support vector machine (SVM), naive Bayes classifier (NBC) and Cox regression (COX), and to optimize the weighting of significant predictors. The subjects recruited for this study were breast cancer patients who had received breast cancer surgery (ICD-9 cm 174–174.9) at one of three southern Taiwan medical centers during the 3-year period from June 2007, to June 2010. The registry data for the patients were randomly allocated to three datasets, one for training (n = 824), one for testing (n = 177), and one for validation (n = 177). Prediction performance comparisons revealed that all performance indices for the DNN model were significantly (p < 0.001) higher than in the other forecasting models. Notably, the best predictor of 10-year survival after breast cancer surgery was the preoperative Physical Component Summary score on the SF-36. The next best predictors were the preoperative Mental Component Summary score on the SF-36, postoperative recurrence, and tumor stage. The deep-learning DNN model is the most clinically useful method to predict and to identify risk factors for 10-year survival after breast cancer surgery. Future research should explore designs for two-level or multi-level models that provide information on the contextual effects of the risk factors on breast cancer survival.