Cardiovascular Disease (CVD) is a leading cause of death worldwide, making accurate and early risk prediction crucial for better patient outcomes. Traditional CVD prediction models often rely on binary decision-making, which struggles with uncertain or borderline cases, leading to misclassification and ineffective treatment strategies. This research proposes an advanced predictive model that combines machine learning algorithms with a three-way decision approach to improve the accuracy and reliability of CVD risk assessment. The three-way decision model, based on rough set theory, divides decisions into three categories acceptance, rejection, and deferment—allowing for more detailed and informed predictions. Using the Cleveland Heart Disease dataset, this study applies machine learning techniques such as Random Forest (97.14% accuracy), Logistic Regression (91.30% accuracy), Naïve Bayes (88.24% accuracy), and Support Vector Machine (89.74% accuracy) to evaluate the model’s effectiveness. The results show that integrating three-way decisions with machine learning improves predictive performance, especially for unclear cases, enhancing clinical decision-making. However, the model’s reliance on dataset quality and threshold selection poses some limitations that need further investigation. This research introduces an intelligent and flexible approach to CVD prediction, which could reduce diagnostic errors and support early interventions for high-risk patients
