Back Propagation Neural Network Model For Medical Expenses In Patients With Breast Cancer

Objective: Breast cancer seriously endangers women's life and health, and brings huge economic burden to the family and society. The aim of this study was to analyze the medical expenses and influencing factors of breast cancer patients, and provide theoretical basis for reasonable control of medical expenses of breast cancer patients. Methods: The medical expenses and related information of all female breast cancer patients diagnosed in our hospitals from 2017 to 2019 were collected. Through SSPS Clementine 12.0 software, the back propagation (BP) neural network model and multiple linear regression model were constructed respectively, and the influencing factors of medical expenses of breast cancer patients in the two models were compared. Results: In the study of medical expenses of breast cancer patients, the prediction error of BP neural network model is less than that of multiple linear regression model. At the same time, the results of the two models showed that the length of stay and region were the top two factors affecting the medical expenses of breast cancer patients. Conclusion: Compared with multiple linear regression model, BP neural network model is more suitable for the analysis of medical expenses in patients with breast cancer.