• Cheng-Mao Zhou, Qiong Xue, Ying Wang, Jianhuaa Tong, Muhuo Ji, and Jian-Jun Yang.
• Department of Anesthesiology, Pain, and Perioperative Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China. zhouchengmao187@foxmail.com.
• Surg. Today. 2021 May 1; 51 (5): 756-763.

PurposeWe used five machine-learning algorithms to predict cancer-specific mortality after surgical resection of primary non-metastatic invasive breast cancer.MethodsThis study was a secondary analysis of data for 1661 women with primary non-metastatic invasive breast cancer. The overall patient population was divided into a training group and a test group at a ratio of 8:2 and python was used for machine learning to establish the prognosis model.ResultsThe machine-learning Gbdt algorithm for cancer-specific death caused by various factors showed the five most important factors, ranked from high to low as follows: the number of regional lymph node metastases, LDH, triglyceride, plasma fibrinogen, and cholesterol. Among the five algorithm models in the test group, the highest accuracy rate was by DecisionTree (0.841), followed by the gbm algorithm (0.838). Among the five algorithms, the AUC values from high to low were GradientBoosting (0.755), gbm (0.755), Logistic (0.733), Forest (0.715), and DecisionTree (0.677).ConclusionMachine learning can predict cancer-specific mortality after surgery for patients with primary non-metastatic invasive breast.

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