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

Abstract

Nowadays, cardiovascular diseases are very common and are considered as the main causes of morbidity and mortality worldwide. Coronary Artery Disease (CAD), the most typical cardiovascular disease is diagnosed by a variety of medical imaging modalities, which have 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.

Selection of 51 predictors from 13,782 candidate multimodal features using machine learning improves coronary artery disease prediction

“…Although such features were not available for our study, additional efforts that include multimodal forms of data input are likely to be of considerable interest. [38][39][40] The performance increase of ML4H EN-COX over FRS, PCE, and QRISK3 can be conceptualized as consisting of ''predictor gain'' and ''modeling gain.'' Predictor gain refers to added predictive value associated with adding more predictors to a model, while modeling gain refers to added predictive value associated with modeling those predictors in more complex ways, such as considering nonlinear relationships between predictors.…”

“…Although such features were not available for our study, additional efforts that include multimodal forms of data input are likely to be of considerable interest. 38 , 39 , 40 …”

Machine Learning Algorithm to Predict Obstructive Coronary Artery Disease: Insights from the CorLipid Trial

“…There is an anticipation that AI will result in a paradigm shift toward precision cardiovascular medicine in the near future [45]. Novel research strategies exploiting the ML powers could help clinicians in the prediction of patients that would benefit from invasive or non-invasive diagnostic modalities [46]. ICA constitutes the gold-standard test for CAD diagnosis; however, better pretest assessment could ultimately improve patient safety and decrease healthcare costs by optimizing referral for outpatient ICA [47].…”