Key result
Older age (≥ 70 years) was strongly associated with a high risk of developing cardiovascular disease, and a Random Forest machine learning model predicted this risk with 75.21% accuracy and an AUC of 80.79%.
Why the study?
To enhance understanding of the influence of type-2 diabetes and underlying determinants on cardiovascular disease risk, and construct precise predictive models in Bangladesh.
Cross-Sectional (n=4,127)
Machine learning algorithms, notably Random Forest, can effectively predict cardiovascular disease risk using demographic and health survey data, highlighting the critical impact of advanced age and elevated BMI in the Bangladeshi population.
Answers are grounded in this paper and cite their sources.
Demonstrates the feasibility of using machine learning on survey data for non-invasive CVD screening; leaves open.
This research aims to enhance our comprehensive understanding of the influence of type-2 diabetes on the development of cardiovascular diseases (CVD) risk, its underlying determinants, and to construct precise predictive models capable of accurately assessing CVD risk within the context of Bangladesh. This study combined data from the 2011 and 2017 to 2018 Bangladesh Demographic and Health Surveys, focusing on individuals with hypertension. CVD development followed World Health Organization (WHO) guidelines. Eight machine learning algorithms (Support Vector Machine, Logistic Regression, Decision Tree, Random Forest, Naïve Bayes, K-Nearest Neighbor, Light GBM, and XGBoost) were analyzed and compared using 6 evaluation metrics to assess model performance. The study reveals that individuals aged 35 to 54 years, 55 to 69 years, and ≥ 70 years face higher CVD risk with adjusted odds ratios (AOR) of 2.140, 3.015, and 3.963, respectively, compared to those aged 18 to 34 years. "Rich" respondents show increased CVD risk (AOR = 1.370, P < .01) compared to "poor" individuals. Also, "normal weight" (AOR = 1.489, P < .01) and "overweight/obese" (AOR = 1.871, P < .01) individuals exhibit higher CVD risk than "underweight" individuals. The predictive models achieve impressive performance, with 75.21% accuracy and an 80.79% AUC, with Random Forest (RF) excelling in specificity at 76.96%. This research holds practical implications for targeted interventions based on identified significant factors, utilizing ML models for early detection and risk assessment, enhancing awareness and education, addressing urbanization-related lifestyle changes, improving healthcare infrastructure in rural areas, and implementing workplace interventions to mitigate stress and promote physical activity.
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Das et al. (2024) conducted a cross-sectional in Hypertension and high risk of developing cardiovascular disease (n=4,127). Age ≥ 70 years vs. Age 18-34 years was evaluated on High risk of developing cardiovascular disease (AOR 3.963, 95% CI 2.838-5.535, p=<0.01). Older age (≥ 70 years) was strongly associated with a high risk of developing cardiovascular disease, and a Random Forest machine learning model predicted this risk with 75.21% accuracy and an AUC of 80.79%.
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