Prediction of Postoperative Survival Level of Esophageal Cancer Patients Based on Kaplan-Meier(K-M) Survival Analysis and Gray Wolf Optimization (GWO)-BP Model

Background: Esophageal squamous cell carcinoma (ESCC) is a global safety problem, especially the low 5-year survival rate of patients after surgery, and their healthy life after surgery is directly threatened. Methods: Kaplan-Meier(K-M) survival analysis is used to screen the blood indexes of patients with ESCC. The gray wolf algorithm (GWO) is introduced to optimize the weight threshold of back-propagation (BP) neural network, and a prediction model based on K-M-GWO-BP is established. Results: According to the influencing factors of postoperative survival, the postoperative survival level of patients is predicted. K-M survival analysis is used to analyze the relevant risk factors, the redundant variables are eliminated, and the whole structure of the neural network is simplified. The initial weight of BP neural network is optimized by GWO. Conclusions: BP neural network model, PSO-BP, GA-BP, SSA-BP, GWO-BP, K-M-BP, K-M-PSO-BP, K-MGA-BP, K-M-SSA-BP and K-M-GWO-BP are compared, the prediction accuracy of K-M-GWO-BP neural network model is the best.