An Electrocardiographic System With Anthropometrics via Machine Learning to Screen Left Ventricular Hypertrophy among Young Adults.

Gen-Min Lin,Kiang Liu

doi:10.1109/jtehm.2020.2990073

Abstract

The prevalence of physiological and pathological left ventricular hypertrophy (LVH) among young adults is about 5%. A use of electrocardiographic (ECG) voltage criteria and machine learning for the ECG parameters to identify the presence of LVH is estimated only 20-30% in the general population. The aim of this study is to develop an ECG system with anthropometric data using machine learning to increase the accuracy and sensitivity for a screen of LVH. In a large sample of 2,196 males, aged 17–45 years, the support vector machine (SVM) classifier is used as the machine learning method for 31 characteristics including age, body height and body weight in addition to 28 ECG parameters such as axes, intervals and voltages to link the output of LVH. The diagnosis of LVH is based on the echocardiographic criteria for young males to be 116 gram/meter2 (left ventricular mass (LVM)/body surface area) or 49 gram/meter2.7 (LVM/body height2.7). On the purpose of increasing sensitivity, the specificity is adjusted around 70-75% and all data tested in proposed model reveal high sensitivity to 86.7%. The area under curve (AUC) of the Precision-Recall (PR) curve is 0.308 in the proposed model which is better than 0.109 and 0.077 using Cornell and Sokolow-Lyon voltage criteria for LVH, respectively. Our system provides a novel screening tool using age, body height, body weight and ECG data to identify most of the LVH among young adults. It provides a fast, accurate and practical diagnosis tool to identify LVH.

Highlights

Artificial intelligence (AI) grows fast with the improvement of technology and the availability of various kinds of big data
The optimized hyperparameter h 0.322 is chosen when the highest area under curve (AUC) of the PR curve averaged from the 4-fold cross validation is found from the values of 981 trials
The performance is assessed by several standard measurements including accuracy, specificity, sensitivity, precision, F1 score, the AUC of the Receiver Operating Characteristic (ROC) curve and the AUC of the PR curve [51], [52]

Summary

Introduction

Artificial intelligence (AI) grows fast with the improvement of technology and the availability of various kinds of big data. An AI of the computational statistics, has been introduced in clinical medicine which could provide accurate diagnosis of disease and prediction of the risk [1]–[12]. In the modern era, using machine learning techniques has become an efficient and reliable tool for clinical practice by physicians globally. Left ventricular hypertrophy (LVH), which is clinically considered as a sign of end-organ damage related to longterm hypertension, has been associated with heart failure and cardiovascular disease events among middle and old-aged individuals [13], [14]. A prior population research shows that the presence of LVH at young ages is associated with higher risk of incident cardiovascular disease events [18]. Over the past 5 years, a few population studies were presented by machine learning and deep learning for the

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