Comparative Analysis of Machine Learning Models vs. Traditional Clinical Calculators for Cardiovascular Risk Prediction
This study demonstrates that a calibrated Gradient Boosting machine learning model, developed using NHANES data and integrated into the "CardioPrediQ" platform, achieves superior composite performance and statistical equivalence to top traditional calculators for predicting cardiovascular mortality, offering a scalable solution particularly beneficial for diverse populations where standard risk tools may lack accuracy.