Predicting Cardiovascular Outcomes: A Comparative Study Of Bayesian And Traditional Methods

This study comprehensively examines and compares current heart disease prediction systems, focusing on Bayesian techniques versus conventional methods. We aim to determine their effectiveness and ease of interpretation in heart health prognosis. We use various statistical and machine learning models to analyze clinical and demographic data from a large patient population collected over time. Bayesian approaches, which handle uncertainty and complex variable interactions, are compared to simpler methods like logistic regression and decision trees. We assess the accuracy, sensitivity, specificity, and AUC-ROC of Bayesian and traditional techniques to evaluate their predictive performance. Additionally, we analyze the models' ability to explain factors influencing heart disease outcomes, crucial for informed treatment decisions. By comparing Bayesian and traditional methodologies, this study helps academics and healthcare practitioners identify optimal modeling methods to enhance patient care and heart disease survival, advancing predictive analytics in healthcare and improving cardiovascular patient management.