ORIGINAL RESEARCH article
Front. Public Health
Sec. Digital Public Health
Volume 13 - 2025 | doi: 10.3389/fpubh.2025.1535056
Physical and Mental Health Management for the Elderly Using XGBoost Algorithm Supported by New Media Technology: Developing Personalized Health Intervention Plans Using Healthcare Data from the CLHLS Database
Provisionally accepted
- 1College of Music and Dance, Guangzhou University, Guangzhou, China
- 2Guangzhou Xinhua University, Dongguan, China
- 3School of Media, Communication and Sociology, University of Leicester, Leicester, East Midlands, United Kingdom
- 4School of Public Administration, Guangzhou University, Guangzhou, China
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This work aims to explore a physical and mental health management scheme for the elderly based on new media technology, providing precise and efficient health management services. First, a personalized digital health management platform for the elderly is designed, integrating multi-source health data and utilizing artificial intelligence and blockchain technology to achieve intelligent, personalized, and secure health management. Given the diverse and complex health information needs of the elderly, this work employs Latent Dirichlet Allocation (LDA) to cluster relevant topics and keywords. This intends to reflect the elderly's need for an in-depth understanding of chronic diseases, including the causes, development process, and potential consequences of the diseases.Next, the text feature vectors extracted from the LDA model are fused with image feature vectors extracted from ResNet50 to form a comprehensive feature representation. Finally, a health risk assessment model is constructed based on the XGBoost algorithm. In the simulation experiments, considering that the China Longitudinal Healthy Longevity Survey (CLHLS) database contains a vast amount of healthcare data, these data can be used to build and validate the health risk assessment
Keywords: New media technology, XGBoost, Elderly, Physical and mental health management, Health intervention
Received: 16 Jan 2025; Accepted: 28 Apr 2025.
Copyright: © 2025 Wang, Guan, Qu, Liao, Ming and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Enhui Li, College of Music and Dance, Guangzhou University, Guangzhou, China
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