Electrocardiographic Machine Learning to Predict Mitral Valve Prolapse in Young Adults

Mitral valve prolapse (MVP), known as balloon mitral valve, accounts for 2-4% of cases in the general population and is associated with several cardiac sequelae. A few studies have shown suboptimal results using electrocardiographic (ECG) machine learning to identify MVP in middle- or old-aged individuals; however, no studies have focused on young adults. The aim of this study is to develop an ECG-based system through machine learning to predict MVP in young adults. In a large military population of 2,206 males, aged 17–43 years, support vector machine (SVM), logistic regression (LR) and multilayer perceptron (MLP) classifiers are used as machine learning techniques for 26 ECG features and additional 6 simple biological parameters to link the output of MVP compared with a traditional ECG criterion of a negative T-axis in inferior limb leads. In the parasternal long-axis view of echocardiography, MVP is defined as a displacement of the anterior or posterior leaflet of the mitral valve to the mid portions of the annular hinge point >2 mm. The values of the area under the receiver operating characteristic curve are 74.59%, 74.16% and 73.02% in the proposed SVM, LR and MLP classifiers, respectively, which are better than 38.13% in the traditional ECG criterion for MVP. Our machine learning system provides a novel tool for screening MVP among young male adults. The proposed method can be an adjuvant to the physical findings for early detection of MVP prior to a confirmation by echocardiography for young male adults