Predicting Cardiovascular Disease Risk Among Bangladeshi Diabetes Patients Using Machine Learning and Explainable AI

Abstract

Cardiovascular disease (CVD) is a leading cause of death, especially among diabetes patients, due to metabolic and lifestyle factors. This study aims to predict cardiovascular disease risk among Bangladeshi diabetes patients using machine learning and explainable AI, focusing on the role of Mediterranean diet adherence. Cardiovascular disease (CVD) is a leading cause of morbidity and mortality in diabetes patients, and early prediction can significantly improve health outcomes. A predictive model based on machine learning algorithms, particularly LightGBM , was developed and evaluated, achieving the highest accuracy of 99.37%. The model was further enhanced with explainable AI techniques to ensure transparency and interpretability of predictions. The dataset utilized clinical, demographic, and lifestyle data, including factors such as triglycerides, sleep patterns, smoking habits, and Mediterranean diet adherence. Results revealed that smoking, sleep hours, and diet adherence were the most influential factors in predicting cardiovascular risk. The model demonstrated strong performance across various evaluation metrics, including precision, recall, and F1-score, further validating its effectiveness. This research underscores the potential of machine learning to transform healthcare by providing early diagnosis tools, allowing for personalized interventions. The model can assist healthcare professionals in identifying at-risk individuals and reducing the burden of cardiovascular diseases in Bangladesh and similar regions, ultimately improving patient outcomes through early detection and targeted-prevention-strategies.