Abstract
Left ventricular diastolic dysfunction (LVDD) occurs at the initial stage of heart failure. Electrocardiographic (ECG) criteria and machine learning for ECG features have been applied to predict LVDD in middle- and old-aged individuals. The purpose of this study is to clarify the performance of machine learning in young adults. Three machine learning classifiers including random forest (RF), support vector machine (SVM) and gradient boosting decision tree (GBDT) for the input of 26 ECG features with or without 6 other biological features (age, anthropometrics and blood pressures) are compared with the corrected QT (QTc) interval, a traditional ECG criterion for LVDD. The definition of LVDD is based on either one of the following echocardiographic criteria: (1) an E/A ratio of mitral inflow 14. The best areas under the receiver operating characteristic curve were observed in machine learning of the RF for ECG only (84.1%) and of the SVM for all ECG and biological features (82.1%), both of which were superior to the QTc interval (64.6%). If the specificity is chosen to be approximately 75.0%, the sensitivity of the RF for ECG only reaches 81.0% and that of the SVM for all ECG and biological features is raised to 85.7%, both of which are higher than 47.6% by the QTc interval. This study suggests that using machine learning for ECG features only or with other biological features to predict LVDD in young Asian adults is reliable. The proposed methods provide for the early detection of LVDD for young adults and are helpful for taking preventive action on heart failure.
Highlights
Left ventricular diastolic dysfunction (LVDD) occurs at the early stage of heart failure [1]
The Coronary Artery Risk Development in Young Adults (CARDIA) study revealed that left ventricular diastolic filling in young adults was related to age, sex, body weight, left ventricular systolic function, heart rate, blood pressure, lung function and physical fitness but not to electrocardiographic (ECG) left ventricular mass index [10], [11]
The largest AUCs of the receiver operating characteristic (ROC) curves were observed in machine learning of the random forest (RF) for ECG only (84.1%) and of the support vector machine (SVM) for all ECG and biological features (82.1%), both of which were superior to the QTc interval (64.6%)
Summary
Left ventricular diastolic dysfunction (LVDD) occurs at the early stage of heart failure [1]. LVDD usually presents at midlife and the prevalence is estimated to be 11%-35% [4]–[6]. Several cardiovascular risk factors such as obesity, arterial hypertension, diabetes, dyslipidemia and coronary heart disease, that commonly develop at midlife have been associated with LVDD [7]–[9]. The prevalence of LVDD among young adults is low, estimated at 1.1% with severe diastolic dysfunction and 9.3% with abnormal relaxation in the Coronary Artery Risk Development in Young Adults (CARDIA) study [10]. The CARDIA study revealed that left ventricular diastolic filling in young adults was related to age, sex, body weight, left ventricular systolic function, heart rate, blood pressure, lung function and physical fitness but not to electrocardiographic (ECG) left ventricular mass index [10], [11].
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