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ORIGINAL RESEARCH article

Front. Public Health
Sec. Health Economics
Volume 12 - 2024 | doi: 10.3389/fpubh.2024.1387255

Novel Concept for the Healthy Population Influencing Factors

Provisionally accepted
Yuhao Shen Yuhao Shen 1Jichao Wang Jichao Wang 2*Lihua Ma Lihua Ma 3*Huizhe Yan Huizhe Yan 3*
  • 1 Inner Mongolia University of Finance and Economics, Hohhot, China
  • 2 School of International Education, Anyang Institute of Technology, Anyang 455000, China
  • 3 School of Management Engineering and Business, Hebei University of Engineering, Handan 056009, China

The final, formatted version of the article will be published soon.

    In the rapid urbanization process in China, due to reasons such as employment, education, and family reunification, the number of mobile population without registered residence in the local area has increased significantly. By 2020, the group had a population of 276 million, accounting for over 20% of the total population, making significant contributions to urban economic development and resource optimization. However, the health status of migrant populations is affected by unique issues such as occupational risks and socio-economic disparities, which play an important role in personal welfare, social stability, and sustainable economic growth.The deterioration of the health of the floating population will lead to a decrease in productivity, an increase in medical expenses, and an increase in pressure on the public health system. In order to analyze and predict the main elements affecting the well-being of transient population, this study uses advanced machine learning algorithms such as principal component analysis, backpropagation (BP) neural networks, community analysis, random forest models, etc. Principal component analysis will identify and extract the most important variables that affect the health status of mobile populations. The BP neural network models the nonlinear interaction between health determinants and health outcomes. Community analysis divides the floating population into different health records and promotes targeted intervention measures. The random forest model improves the accuracy and universality of predictions. The insights generated by these models will help develop health policies and intervention policies to improve the health status of mobile populations, narrow disparities, and promote social and economic stability. Integrating data-driven methods and emphasizing a shift towards correct, effective, and impactful public health management provides a robust framework for understanding and addressing the complex health issues faced by mobile populations.

    Keywords: Migrant population, Health determinants, machine learning algorithms, public health policy, Predictive Modeling

    Received: 17 Feb 2024; Accepted: 07 Oct 2024.

    Copyright: © 2024 Shen, Wang, Ma and Yan. 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:
    Jichao Wang, School of International Education, Anyang Institute of Technology, Anyang 455000, China
    Lihua Ma, School of Management Engineering and Business, Hebei University of Engineering, Handan 056009, China
    Huizhe Yan, School of Management Engineering and Business, Hebei University of Engineering, Handan 056009, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.