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Machine Learning Approaches to Identify Influential Factors of the Comorbid Psychiatric Symptoms and Hypertension
Mohammad Rocky Khan Chowdhury1,2, Md Mobarak Hossain Khan3, Ali Ahmed4,5
1Department of Population Science Jatiya Kabi Kazi Nazrul Islam University Mymensingh Bangladesh.
Insights
In rural Bangladesh, 8.7% of individuals experience both psychiatric symptoms and hypertension. Key factors include cardiovascular disease, smoking, and a family history of hypertension, highlighting a need for improved public health initiatives.
Area of Science:
- Public Health
- Epidemiology
- Computational Medicine
Background:
- Psychiatric symptoms (anxiety/depression) and hypertension are significant public health issues in Bangladesh, particularly in rural areas with limited healthcare.
- Comorbidity of these conditions presents a substantial challenge, necessitating research into associated factors.
Purpose of the Study:
- To identify influential factors associated with comorbid psychiatric symptoms and hypertension in rural Bangladesh.
- To apply machine learning (ML) algorithms for analyzing this comorbidity.
Main Methods:
- A cross-sectional survey of 1603 respondents using multistage random sampling.
- Application and evaluation of six machine learning algorithms.
- Variable importance analysis using SHapley Additive exPlanations (SHAP).
Main Results:
- The prevalence of comorbid psychiatric symptoms and hypertension was 8.7%.
- The Support Vector Machine (SVM) model showed the best predictive performance (accuracy 0.748).
- Key associated factors identified include cardiovascular disease, family history of hypertension, smoking, chronic disease, and tobacco use.
Conclusions:
- Approximately one in ten rural individuals in Bangladesh experience comorbid psychiatric symptoms and hypertension.
- Machine learning models identified significant associated factors, though predictive performance was modest.
- There is an urgent need for enhanced national and regional public health initiatives in rural Bangladesh to address this comorbidity.
Background And Aim:
The burden of both psychiatric symptoms (anxiety and/or depression) and hypertension poses significant public health challenges in Bangladesh, especially in rural areas with limited healthcare access. Thus, this study aimed to identify the influential factors associated with comorbid psychiatric symptoms and hypertension in rural Bangladesh using machine learning (ML) algorithms.
Methods:
In this study, 1603 respondents were selected from a cross-sectional survey using a multistage random sampling method. Six commonly used ML algorithms were applied. The ML models' predictive performance was evaluated using standard validation metrics. Influential variables were ranked and explained using SHapley Additive exPlanations (SHAP) method.
Results:
The prevalence of comorbid psychiatric symptoms and hypertension was 8.7%. In predicting this outcome, the SVM model outperformed others across a variety of metrics: accuracy (0.748), RMSE (0.520), specificity (0.708), and Brier score (0.252). The model achieved a modest receiver operative characteristics (ROC) score of 0.613 (95% CI: 0.532-0.690) for predicting comorbidity. The top factor associated with this comorbid condition, as explained by the SHAP method, included respondents with cardiovascular disease (CVD), family history of hypertension, current smoking exposure, chronic disease, and current tobacco user.
Conclusion:
Approximately one out of ten people in rural areas experienced comorbid psychiatric symptoms and hypertension. The ML models highlighted several key associated factors, although their predictive performance was modest. The current situation highlights an urgent need for strengthened national and regional public health initiatives in rural Bangladesh.
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