Prediction Model of Elderly Care Willingness Based on Machine Learning

Yongchao Jin (), Dongmei Liu, Kenan Wang, Renfang Wang and Xiaodie Zhuang
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Yongchao Jin: College of Sciences, North China University of Science and Technology, Tangshan 063210, China
Dongmei Liu: College of Sciences, North China University of Science and Technology, Tangshan 063210, China
Kenan Wang: College of Sciences, North China University of Science and Technology, Tangshan 063210, China
Renfang Wang: College of Sciences, North China University of Science and Technology, Tangshan 063210, China
Xiaodie Zhuang: College of Sciences, North China University of Science and Technology, Tangshan 063210, China

Mathematics, 2023, vol. 11, issue 3, 1-18

Abstract: At present, the problem of an aging population in China is severe. The integration of existing healthcare services with elderly care services is inefficient and cannot meet the needs of the elderly. As such, China urgently needs the concerted efforts of various social forces to cope with the increasingly serious problem of aging. In accordance with Andersen’s behavioral model, a survey was conducted in Tangshan City among seniors 60 years of age and older. Using logistic regression models, decision tree models, and random forest models, we examined the factors impacting senior people’s desire to choose the integrated medical care and nursing care model. The results of the three models displayed that the elderly’s propensity to choose the combined medical care and nursing care model is significantly influenced by the amount of insurance, life care needs, and healthcare needs. Moreover, the study found that the willingness of the elderly in Tangshan to improve the combined medical and nursing care service system is low. The government should appeal to the community to participate in multiple developments to improve the integrated medical and nursing service system.

Keywords: healthcare integration; elderly care; logistic regression; decision tree; random forest (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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