What is the clinical evidence from this study?

Study design: Cross-Sectional. Population: Hypertension with anxiety and depression symptoms (n=496). Intervention: Extreme Gradient Boosting (XGB) model vs. Logistic Regression (LR) model. Primary outcome: Predictive performance (ROC) for anxiety symptoms (95% CI 91.5-94.6).

September 10, 2025Frontiers in Psychology1 citationsOpen Access

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

ZRZarin RaihanaUniversity of Rajshahi MKManzur KaderDalarna University MIMd. Zahidul IslamKwangwoon University

Key Result

The Extreme Gradient Boosting model outperformed traditional logistic regression in predicting anxiety (ROC 93.1% vs 83.8%) and depression (ROC 90.7% vs 79.7%) in rural hypertensive adults.

Study Design

Type

Cross-Sectional (n=496)

Multicenter

Structured PICO

Does the Extreme Gradient Boosting (XGB) machine learning model improve the prediction of anxiety and depression symptoms compared to logistic regression in rural hypertensive adults?

Population

496 rural adults with hypertension in Bangladesh

Intervention

Extreme Gradient Boosting (XGB) machine learning model for prediction

Comparator

Traditional logistic regression (LR) model

Outcome

Presence of anxiety and depression symptoms evaluated using the Generalized Anxiety Disorder-7 (GAD-7) and Patient Health Questionnaire-9 (PHQ-9) scalespatient reported

An Extreme Gradient Boosting machine learning model outperformed traditional logistic regression in predicting anxiety and depression symptoms among rural hypertensive adults, identifying key sociodemographic and clinical risk factors.

Main Result

Absolute Event Rate: 93.1% vs 83.8%

Limitations

Retrospective data collection and reliance on self-reporting introduces potential for underreporting and recall bias
Omission of important behavioral factors such as sleep problems, physical activity, and eating behaviors
Cross-sectional design prevents the establishment of causal associations
Small sample size may not be representative, potentially affecting generalizability
Machine learning models employed lack the capability to generate p-values for examining associations

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.

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