Evidence map›Paper›PMID 41000527›Full record

ArticleFrontiers in psychology2025

Factors associated with the presence of anxiety and depression symptoms in rural hypertensive adults in Bangladesh: leveraging extreme gradient booster machine learning algorithm.

Zarin Raihana, Manzur Kader, Md Zahidul Islam, Farzana Akhter Bornee, Md Nazrul Islam Mondal, Mohammad Rocky Khan Chowdhury, Baki Billah

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

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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Zarin RaihanaDepartment of Clinical Psychology, Faculty of Biological Sciences, University of Rajshahi, Rajshahi, Bangladesh.
Manzur KaderDepartment of Medical Science, School of Health and Welfare, Dalarna University, Falun, Sweden.
Md Zahidul IslamDepartment of Public Health, First Capital University of Bangladesh, Khulna, Bangladesh.
Farzana Akhter BorneeDepartment of Pediatrics, Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh.
Md Nazrul Islam MondalDepartment of Population Science and Human Resource Development, University of Rajshahi, Rajshahi, Bangladesh.
Mohammad Rocky Khan ChowdhuryDepartment of Public Health, First Capital University of Bangladesh, Khulna, Bangladesh.
Baki BillahDepartment of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Anxiety and depression are common among hypertensive patients and can lead to significant health complications. This study aimed to use Extreme Gradient Boosting (XGB) machine learning (ML) technique to select associated factors of anxiety and depression symptoms among people with hypertension in rural areas. Methodology: A cross-sectional study was conducted using a multistage cluster random sampling. The anxiety and depression symptoms were evaluated using the Generalized Anxiety Disorder-7 (GAD-7) and Patient Health Questionnaire-9 (PHQ-9) scales, respectively. A chi-square test was performed to assess prevalence. XGB model was employed to predict the presence of anxiety and depression symptoms using 13 variables, and the model's performance was compared with that of the traditional logistic regression (LR) model. Influential variables were explained and ranked using SHapley Additive exPlanations (SHAP) technique. Results: Among the 496 rural hypertensive adults, approximately 5.9% and 6.4% experienced the presence of anxiety and depression symptoms, respectively. Anxiety and depression symptoms were more prevalent among higher educated patients (14.0%) and who used tobacco (12.4%), respectively. The XGB model demonstrated improved predictive performance (for anxiety, ROC for XGB: 93.1%; for depression, ROC for XGB: 90.7%) compared to the LR model (for anxiety, ROC for LR: 83.8%; for depression, ROC for XGB: 79.7%) in predicting both outcomes. Marital status, body mass index (BMI), cardiovascular disease (CVD), educational status, family history of hypertension and employment were the influential factors in predicting the presence of anxiety symptoms. Similarly, chewing tobacco, family history of hypertension, marital status, CVD, sex, and educational status are important factors in predicting the presence of anxiety. Conclusion: In Bangladesh, around 6% rural individuals with hypertension experienced the presence of anxiety and depression symptoms. Educational status, marital status, CVD and family history of hypertension were key factors linked to both outcomes. Future research is needed to validate these findings.

Indexed as

anxietydepressionhypertensionlogistic regressionmachine learning

Identifiers

PMID41000527
PMCPMC12457397

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