Evidence map›Paper›PMID 41469871›Full record

ArticleBMC public health2025

How perceived stress and social support shape non-communicable disease risks beyond traditional factors: a machine learning perspective.

Xu Deng, Shulin Chen, Jiang Xue

Abstract read
In one paragraph

Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Xu DengDepartment of Psychology and Behavioral Sciences, Zijingang Campus of Zhejiang University, NO. 866 Yuhangtang Road, Hangzhou, Zhejiang, China.
Shulin ChenDepartment of Psychology and Behavioral Sciences, Zijingang Campus of Zhejiang University, NO. 866 Yuhangtang Road, Hangzhou, Zhejiang, China. chenshulin@zju.edu.cn.
Jiang XueThe Affiliated Dongguan Songshan Lake Central Hospital, Guangdong Medical University, Songshan Lake Science Park, Dongguan, Guangdong, China. jiang_xue@gdmu.edu.cn.

Funding

Basic and Applied Basic Research Foundation of Guangdong Province 2023A1515110169China Postdoctoral Science Foundation 2020TQ0265Humanities and Social Sciences Youth Foundation, Ministry of Education 22YJCZH209
6 · The paper itself

Abstract

backgroundPsychosocial factors such as perceived stress and social support have been increasingly recognized as significant contributors to non-communicable diseases (NCDs). However, their predictive value in comparison to traditional risk factors remains insufficiently explored. This study used machine learning-based random forest models to compare the relative importance of psychosocial factors and traditional risk factors in predicting NCD risk.

methodsCross-sectional data were collected from March to June 2021 from a sample of 2567 participants aged 40 years or older in Ningbo City, China. Random forest models were constructed to predict the occurrence of NCDs, including hypertension, diabetes, hyperlipidemia, and chronic respiratory diseases. Model performance was evaluated using accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). The importance of features was assessed using both mean decrease in Gini and permutation importance metrics.

resultsThe results demonstrated that random forest models exhibited satisfactory performance in predicting various types of NCDs, achieving accuracies ranging from 0.662 to 0.805 and AUC values from 0.642 to 0.728. Moreover, subjective social support and perceived stress consistently ranked as the fourth, fifth, or sixth most influential factors after age, BMI, and waist circumference but ahead of smoking and salt intake.

conclusionsThe findings unveiled the pivotal roles of psychosocial factors, such as perceived stress and subjective social support, in the prevention and management of NCDs. Notably, these psychosocial factors frequently ranked among the top predictors, demonstrating predictive importance comparable to that of established traditional risk factors. It is recommended that prevention and management guidelines for NCDs incorporate solid components targeting these psychosocial factors.

Indexed as

Machine LearningNoncommunicable DiseasesSocial SupportStress, PsychologicalAdultAgedChinaCross-Sectional StudiesFemaleHumansMaleMiddle AgedRisk FactorsMachine learningMental stressNon-communicable diseaseRandom forestSocial support

Identifiers

PMID41469871
PMCPMC12754945

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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.