Research and Prediction of Health Expenditure Factors in China Based on Machine Learning Methods

DOI

10.2991/978-94-6463-040-4_112How to use a DOI?

Keywords

Machine learning; Elman Neural Network; Principal component analysis; Prediction of health expenditure

Abstract

Objective

To analyze the influencing factors of China's total health expenditure and predict the development trend of China's total health expenditure in the next five years based on historical statistics and provide some theoretical basis for formulating relevant policies. Methods: The main factors that may influence the total health expenditure in China were selected by the theory of health demand-supply relationship, and the degree of influence of the factors influencing the total health expenditure in China was studied by using gray correlation and principal component analysis (PCA). Based on the analysis results of the two methods and the comparative analysis of the fit of the various models, the machine learning model was finally applied to forecast the total health expenditure in China in the next five years. Results: The total health expenditure from 2021 to 2025 are 78663.60, 83950.43, 88748.01, 93397.45, and 97974.29 billion yuan, respectively. The forecast results indicate that the total health expenditure in China will continue to maintain the growth trend in the next five years, but the growth rate will gradually level off. Conclusion: Both gray correlation and PCA analysis show that the influencing factors of China's total health expenditure are mainly reflected in economic income and medical services. Therefore, it is necessary to ensure the coordinated development of total health expenditure and economy, and improve the financing structure of total health expenditure. Improve the medical service system and optimize the allocation of health resources.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Xiaoqin Zhang
AU  - Xiaowen Wan
PY  - 2022
DA  - 2022/12/27
TI  - Research and Prediction of Health Expenditure Factors in China Based on Machine Learning Methods
BT  - Proceedings of the 2022 3rd International Conference on Artificial Intelligence and Education (IC-ICAIE 2022)
PB  - Atlantis Press
SP  - 737
EP  - 744
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-040-4_112
DO  - 10.2991/978-94-6463-040-4_112
ID  - Zhang2022
ER  -