Feiyan Ruan1,2, Xiaotong Ding3, Huiping Li1, Yixuan Wang1, Kemin Ye1, Houming Kan4. 1. School of Nursing, Anhui Medical University, Hefei 230032, China. 2. Breast surgery, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China. 3. School of Nursing, Nanjing Medical University, Nanjing 211166, China. 4. Pain department, SIR RUN RUN Hospital of Nanjing Medical University, Nanjing 211166, China.
Abstract
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.
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 cancerpatients, and provide theoretical basis for reasonable control of medical expenses of breast cancerpatients. METHODS: The medical expenses and related information of all female breast cancerpatients 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 cancerpatients in the two models were compared. RESULTS: In the study of medical expenses of breast cancerpatients, 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 cancerpatients. 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.
Entities:
Keywords:
back propagation neural network ; breast cancer ; influencing factor ; medical expenses ; multiple linear regression