Determinants of Developing Cardiovascular Disease Risk with Emphasis on Type-2 Diabetes and Predictive Modeling Utilizing Machine Learning Algorithms

Background 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. Methods This study combined data from the 2011 and 2017-18 Bangladesh Demographic and Health Surveys, focusing on individuals with hypertension. CVD development followed 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 six evaluation metrics to assess model performance. Results The study reveals that individuals aged 35–54 years, 55–69 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 18–34 years. 'Rich' respondents show increased CVD risk (AOR = 1.370, p < 0.01) compared to 'poor' individuals. Also, 'normal weight' (AOR = 1.489, p < 0.01) and 'overweight/obese' (AOR = 1.871, p < 0.01) 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%. Conclusion 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.