Accuracy of machine learning techniques to predict stress echocardiography results using clinical variables

Abstract Background Stress echocardiography (SE) is a diagnostic prognostic imaging modality for suspected coronary artery disease (CAD). Cardiovascular risk factors (CVRF) are used in the assessment of the probability of CAD. The link between the outcome of SE and patients’ variables including CVRF, current medication, and anthropometric variables has not been widely investigated.Objective: The aim of this study is to understand whether a machine learned algorithm can be used to predict significant CAD defined by inducible ischemia on SE in patients with suspected CAD based on anthropometrics, CVRF, and medication as variables. Methods A machine learning (ML) framework was generated to automate the prediction of inducible ischemia on stress echocardiography results. A mutual information-based feature selection methods (support vector machine (SVM) and random forest (RF) classifiers) were used to investigate the amount of information that each feature carried to define the positive outcome of SE.Data from 2201 patients were used to train and validate the framework. Patients mean age was 62 (SD 11) years. The data consists of anthropological data and classical CVRF, atrial fibrillation, prior diagnosis of CAD, stroke, obstructive airways disease, chronic inflammatory disease, chronic kidney disease (CKD>3) and prescribed medications at the time of the test. There were 394 positive and 1807 negative SE cases. Five-fold cross-validation was used to validate the performance of the framework. Results The feature selection methods showed that prescribed medications such as Weight, Diabetes, Antiplatelet drugs and ACE- inhibitor/ARB, were the features that shared the most information about the outcome of SE (sensitivity and specificity of 81% and 51% and 81% and 61% for SVM and RF, respectively) with the accuracy of 82%. Conclusion This study shows that ML can predict the outcome of SE based on only 4 clinical features. Further research correlating the clinical variable and SE result to cardiovascular outcome will improve the model’s clinical utility and facilitate patient selection for early treatment avoiding unnecessary downstream testing.