Journal of Coronary Heart Diseases

Department of Cardiology, University of Paris-Saclay, F-75252, Saclay, France

Publications

• Mini Review   
  Machine Learning and Artificial Intelligence Models for Predicting Coronary Artery Disease Risk: Comparative Analysis of Performance and Interpretability
  Author(s): Ralph Maddison*
  
  Coronary artery disease remains a leading cause of morbidity and mortality worldwide. With the rapid advancement of machine learning and artificial intelligence techniques, there has been an increasing interest in using these methods for CAD risk prediction. This study aims to provide a comprehensive comparative analysis of various ML and AI models for predicting CAD risk, considering both their performance and interpretability. A diverse dataset containing clinical, demographic, and diagnostic features was used to train and evaluate the models. The models' performance was assessed using standard evaluation metrics, including accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve. Additionally, model interpretability was evaluated using techniques such as feature importance analysis and SHAP (SHapley Additive exPlanati.. Read More»
  DOI: 10.37421/2684-6020.2023.7.181

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