Determinants of developing cardiovascular disease risk with emphasis on type-2 diabetes and predictive modeling utilizing machine learning algorithms.

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.