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Cardiovascular risk prediction and influencing predictors identification among Bangladeshi individuals using machine
Md Merajul Islam1, Sujit Kumar1, Md A Salam2
1Department of Statistics, Jatiya Kabi Kazi Nazrul Islam University, Mymensingh, Bangladesh.
Insights
Machine learning accurately predicts cardiovascular disease (CVD) risk in Bangladesh. Key predictors include age, urban living, wealth, and air conditioning use, enabling targeted prevention strategies.
Area of Science:
- Public Health
- Biomedical Informatics
- Machine Learning in Healthcare
Background:
- Cardiovascular disease (CVD) is a leading global cause of death, significantly impacting Bangladesh.
- Predictive modeling using machine learning (ML) offers a promising approach for early CVD detection and risk stratification.
- Identifying high-risk individuals is crucial for developing targeted public health interventions.
Purpose of the Study:
- To predict high-risk individuals for CVD in Bangladesh using ML algorithms.
- To identify key influencing predictors of high CVD risk through association mining.
- To enhance CVD prediction precision and inform prevention strategies.
Main Methods:
- Utilized Bangladesh Demographic and Health Survey (BDHS) 2022 data (n=2,221).
- Employed Boruta-based feature selection to identify significant CVD risk predictors.
- Applied and evaluated multiple ML models (Logistic Regression, Naïve Bayes, ANN, Random Forest, XGBoost) for prediction.
- Analyzed influencing predictors using association mining rules.
Main Results:
- Boruta identified age, residence, marital status, wealth, AC ownership, and BMI as key predictors.
- Extreme Gradient Boosting (XGB) model demonstrated superior performance (Accuracy: 68.22%, AUC: 0.721).
- Association rules highlighted older age (≥65), urban residence, richest wealth, AC ownership, and widowhood as significant risk factors.
Conclusions:
- XGBoost shows strong potential for predicting high CVD risk in the Bangladeshi population.
- Identified key demographic and socioeconomic factors contributing to CVD risk.
- Findings facilitate the development of targeted CVD prevention and management strategies.
Background:
Cardiovascular disease (CVD) encompasses a group of disorders that affect the heart and blood vessels, making it one of the leading causes of death globally, including in Bangladesh. Applying predictive modeling for the early identification and detection of CVD holds significant promise for saving lives by enhancing prediction precision through machine learning algorithms. Therefore, this study aimed to predict high-risk individuals for CVD using machine learning algorithms and identify its influencing predictors by association mining rules among individuals in Bangladesh.
Materials And Methods:
This study utilized the most recent Bangladesh Demographic and Health Survey (BDHS) 2022 data, which encompassed 2,221 respondents. A Boruta-based feature selection method is employed to determine the important features associated with the high risk of CVD. Different machine learning algorithms, including logistic regression, Naïve Bayes, artificial neural network, random forest, and extreme gradient boosting (XGB), are adopted to predict the high-risk individuals for CVD in the training dataset. The predictive performance of the models is evaluated using accuracy, precision, recall, F1-score, and area under the curve (AUC) in the testing set. Additionally, the most significant rules are analyzed using the association mining technique to identify the influencing predictors of high risk of CVD.
Results:
The Boruta method indicated that age, residence, marital status, wealth, having an air conditioner (AC), and body mass index (BMI) are important predictors of high risk of CVD. The XGB-based predictive model achieves impressive performance compared to other models, with an accuracy of 68.22%, precision of 69.70%, F1-score of 79.54%, and AUC of 0.721. The association rules identified that being aged 65 or older, living in an urban area, having the richest wealth status, having AC, and being widowed are the influencing predictors of high risk of CVD.
Conclusions:
This study emphasizes the potential of XGB in predicting high-risk individuals for CVD and enhances the investigation of key factors contributing to CVD risk in this population, thereby facilitating the development of targeted prevention strategies that can effectively mitigate the high CVD risk.
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