A Machine Learning Approach for the Prediction of the Progression of Cardiovascular Disease based on Clinical and Non-Invasive Imaging Data.

Nowadays, cardiovascular diseases are very common and are considered as the main cause of morbidity and mortality worldwide. Coronary Artery Disease (CAD), the most typical cardiovascular disease is diagnosed by a variety of medical imaging modalities, which involve costs and complications. Therefore, several attempts have been undertaken to early diagnose and predict CAD status and progression through machine learning approaches. The purpose of this study is to present a machine learning technique for the prediction of CAD, using image-based data and clinical data. We investigate the effect of vascular anatomical features of the three coronary arteries on the graduation of CAD. A classification model is built to predict the future status of CAD, including cases of "no CAD" patients, "non-obstructive CAD" patients and "obstructive CAD" patients. The best accuracy was achieved by the implementation of a tree-based classifier, J48 classifier, after a ranking feature selection methodology. The majority of the selected features are the vessel geometry derived features, among the traditional risk factors. The combination of geometrical risk factors with the conventional ones constitutes a novel scheme for the CAD prediction.