Abstract
BackgroundLeft atrial enlargement (LAE) is associated with cardiovascular events. Machine learning for ECG parameters to predict LAE has been performed in middle- and old-aged individuals but has not been performed in young adults.MethodsIn a sample of 2,206 male adults aged 17–43 years, three machine learning classifiers, multilayer perceptron (MLP), logistic regression (LR), and support vector machine (SVM) for 26 ECG features with or without 6 biological features (age, body height, body weight, waist circumference, and systolic and diastolic blood pressure) were compared with the P wave duration of lead II, the traditional ECG criterion for LAE. The definition of LAE is based on an echocardiographic left atrial dimension > 4 cm in the parasternal long axis window.ResultsThe greatest area under the receiver operating characteristic curve is present in machine learning of the SVM for ECG only (77.87%) and of the MLP for all biological and ECG features (81.01%), both of which are superior to the P wave duration (62.19%). If the sensitivity is fixed to 70–75%, the specificity of the SVM for ECG only is up to 72.4%, and that of the MLP for all biological and ECG features is increased to 81.1%, both of which are higher than 48.8% by the P wave duration.ConclusionsThis study suggests that machine learning is a reliable method for ECG and biological features to predict LAE in young adults. The proposed MLP, LR, and SVM methods provide early detection of LAE in young adults and are helpful to take preventive action on cardiovascular diseases.
Highlights
Machine learning, an artificial intelligence (AI)-based computational statistic, has been broadly applied to clinical practice in medicine to assess disease risk and diagnosis [1–12]
Prior studies [29–31] have revealed that machine learning for ECG features could detect most of the Left atrial enlargement (LAE) cases from hospitalized patients, probably due to those patients with LAE who were likely to have other cardiac comorbidities, such as heart failure, that were reflected by ECG features; the results might not be appropriate for healthy individuals
This study revealed that the P wave axis rather than the P wave duration was a strong indicator for LAE
Summary
An artificial intelligence (AI)-based computational statistic, has been broadly applied to clinical practice in medicine to assess disease risk and diagnosis [1–12]. LAE is a precursor of left atrial dysfunction and has been associated with incident atrial fibrillation, ischemic stroke, and cardiovascular events in middle- and old-aged individuals [18– 21]. A prior coronary artery disease risk development in young adults (CARDIA) study revealed that the presence of LAE at a young age is a risk factor in incident cardiovascular events occurring in midlife [25]. Early detection of LAE is vital to prevent the development of cardiovascular diseases and related sequelae. Left atrial enlargement (LAE) is associated with cardiovascular events. Machine learning for ECG parameters to predict LAE has been performed in middle- and old-aged individuals but has not been performed in young adults
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