Factors associated with the presence of anxiety and depression symptom in rural hypertensive adults in Bangladesh: Leveraging Extreme Gradient Booster Machine Learning approach

Zarin Raihana¹Manzur Kader^2*Md. Zahidul Islam^1,3Farzana Akhter Bornee⁴Nazrul Islam Mondal⁵Mohammad Rocky Khan Chowdhury^3,6Md Billah⁶

• ¹University of Rajshahi Faculty of Biological Sciences, Rajshahi, Bangladesh
• ²Dalarna University, Falun, Sweden
• ³First Capital University of the Bangladesh, Chuadanga, Bangladesh
• ⁴Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh
• ⁵University of Rajshahi Department of Population Science and Human Resource Development, Rajshahi, Bangladesh
• ⁶Monash University School of Public Health and Preventive Medicine Division of Clinical Epidemiology, Melbourne, Australia

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 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.