Work

Analyzing factors influencing hospitalization costs for five common cancers in China using neural network models

Year: 2025

Type: article

Abstract: Introduction Malignant tumors are a major global health crisis, causing 25% of deaths in China, with lung, liver, thyroid, breast, and colon cancers being the most common. Understanding the factors influencing hospitalization costs for these cancers is crucial for public health and economics. This study aimed to identify key cost factors and develop a neural network model for predicting hospitalization costs, thereby providing tools to ease the financial burden on patients and healthcare systems.Methods Data on hospitalization costs for 30,893 cancer patients from secondary or higher-level hospitals in Zhuhai, Guangdong Province, between 2017 and 2022, were analyzed. Neural network classification and feature importance analysis were used to determine the main factors influencing costs and to develop predictive models.Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC), with a 95% confidence interval (CI) calculated for the AUROC value.Results The key factors influencing hospitalization costs for lung cancer are metastasis and malignant solid tumor (MST), with correlation coefficients of 0.126 and 0.086, respectively, both showing statistical significance (P < 0.05). For colon cancer, the key factors influencing hospitalization costs are mortality and coronary disease (CD), with correlation coefficients of 0.092 and 0.090, respectively, both demonstrating statistical significance (P < 0.05). The AUROC value for the lung cancer model is 0.9078 (95%CI: 0.8975–0.9186), and the AUROC value for the colon cancer model is 0.9017 (95% CI: 0.8848–0.9196).Conclusion This study confirmed the strong clinical applicability of the neural network predictive model in analyzing hospitalization costs for lung and colon cancer and revealed the factors that influence hospitalization costs for these cancers.

Source: Journal of Medical Economics

DOI: 10.1080/13696998.2025.2494459