Evidence map›Paper›PMID 41212830›Full record

ArticlePloS one2025

Identifying key determinants of health among China's migrant population using machine learning methods: Evidence from the china migrants dynamic survey.

Bo Dong, Yuxin Zhou, Li Wang, Yiyu Wang, Zhenlin Zhang

Expression of concernAbstract read
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Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It carries an expression of concern. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Bo DongSchool of Public Health, Zhejiang Chinese Medicine University, Hangzhou, Zhejiang Province, China.ORCID https://orcid.org/0009-0009-2214-5706
Yuxin ZhouSchool of Marxism, Xi'an Jiaotong University, Xi'an, Shaanxi Province, China.ORCID https://orcid.org/0009-0008-1105-9354
Li WangSchool of nursing, Qilu Medical University, Zibo, Shandong Province, China.
Yiyu WangJ.E. Cairnes School of Business & Economics, University of Galway, University Road, Galway, Ireland.ORCID https://orcid.org/0009-0001-5641-841X
Zhenlin ZhangChina Jiliang University College of Modern Science and Technolog, Yiwu, Zhejiang Province, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundContinuously improving health security for the migrant population is a key component of China's healthcare system reform. Existing research indicates that migrant health is influenced by multiple factors, yet the relative importance of these factors remains inadequately measured. This study aims to analyze the current health status of China's migrant population and rank the primary factors influencing their health based on importance.

methodsData were sourced from the 2018 China Migrants Dynamic Survey, including 108,669 cases after data cleaning. The health status of the migrant population was initially analyzed using frequency and percentage distributions. Logistic regression was then applied to examine the relationship between various factors and migrant health. Subsequently, six machine learning methods (Neural Network, Random Forest, Support Vector Machine, Gradient Boosting Machine, Extra Trees, and Decision Tree) were applied to rank the importance of these factors. A multidimensional performance metric system (accuracy, precision, recall, F1 score, and AUC value) was employed to comprehensively evaluate the classification performance of the models. SHAP (Shapley Additive Prediction) values were used to illustrate the contribution of different factors to the health status of the migrant population.

resultsThe health status of China's migrant population is generally positive, though it is influenced by multiple factors, with varying degrees of significance. Among six distinct machine learning models, the Random Forest model demonstrated the best predictive performance. Its results indicate that the key factors affecting migrant health are age, employment, income, and education level. SHAP value analysis reveals that stable employment, higher education levels, and higher income are positively correlated with better health outcomes, while age was predominantly negatively correlated, indicating a detrimental effect on health status.

conclusionThe overall health status of China's migrant population is relatively optimistic. However, their disadvantaged positions in areas such as education and income expose them to higher health risks. To address these key determinants, further improvements in health safeguards should focus on: developing stratified intervention strategies based on age structure differences; optimizing work environments and employment security; enhancing health literacy; and strengthening public health emergency management and social support systems.

Indexed as

Health StatusMachine LearningTransients and MigrantsAdolescentAdultChinaFemaleHumansMaleMiddle AgedSurveys and QuestionnairesYoung Adult

Identifiers

PMID41212830
PMCPMC12599968

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.