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

Ihekwaba, Ugochukwu; Bennasar, Mohamed; Johnson, Nicholas; Price, Blaine; Oke, Jason; Khoo, Jeffery; Squire, Iain and Kardos, Attila (2023). Accuracy of machine learning techniques to predict stress echocardiography results using clinical variables. In: Heart, 109 A297-A298.

DOI: https://doi.org/10.1136/heartjnl-2023-BCS.285

Abstract

Background Stress echocardiography is a well-established diagnostic prognostic modality with good predictive power in patient with suspected coronary artery disease (CAD). Cardiovascular risk factors are used in the assessment of the probability of CAD. The link between the outcome of stress echocardiography and patients’ variables has not been widely investigated.
Objective The aim of this study aim is to understand whether new algorithm can be developed using machine learning principle to predict stress echocardiography result in patients with suspected CAD based on clinical variables. This could facilitate a reclassification of patients’ risk prediction of CAD.
Methods A machine learning framework was generated to automate the prediction of inducible ischemia on stress echocardiography results. The framework consisted of four stages: feature extraction, pre-processing, feature selection, and classification stage. A mutual information‘based feature selection method was used to investigate the amount of information that each feature carried to define the positive outcome of stress echocardiography. Two classification algorithms, support vector machine (SVM) and random forest classifiers, have been deployed.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 cardiovascular risk factors such as gender, age, weight, family history, diabetes, smoking history, hypertension, hypercholesterolemia, atrial fibrillation, prior diagnosis of CAD, prior bypass grafting, history of coronary intervention, stroke, airways disease, chronic inflammatory disease, chronic kidney disease (>3) and prescribed medications at the time of the test. There were 394 positive and 1807 negative stress echocardiography results. The framework was evaluated using the whole dataset including cases with prior diagnosis of CAD. Five-fold cross-validation was used to validate the performance of the framework. We also investigated the model in the subset of patients with no prior CAD.
Results The feature selection methods showed that prescribed medications such as antiplatelet and angiotensin-converting enzyme inhibitor, weight, and diabetes were the features that shared the most information about the outcome of stress echocardiography. Random forest classifiers showed the best trade-off between sensitivity and specificity and was achieved with three features. Using only four features, we achieved an accuracy of 82.13%.
Conclusions This study shows that machine learning can predict the outcome of stress echocardiography based on only a few features. Further research correlating the clinical variable and stress echo result to cardiovascular outcomes improve the performance of the proposed algorithm with the potential of facilitating patient selection for early treatment/intervention avoiding unnecessary downstream testing.

Plain Language Summary

Machine learning techniques can be used to prioritise stress echocardiograms for patients most likely to have a cardiovascular issue.

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