Improvement of electrocardiographic diagnostic accuracy of left ventricular hypertrophy using a Machine Learning approach

• Published in: PloS one Vol. 15; no. 5; p. e0232657
• Main Authors: De la Garza-Salazar, Fernando, Romero-Ibarguengoitia, Maria Elena, Rodriguez-Diaz, Elias Abraham, Azpiri-Lopez, Jose Ramón, González-Cantu, Arnulfo
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 13.05.2020; Public Library of Science (PLoS)
• Subjects: Abnormalities; Adult; Aged; Aged, 80 and over; Algorithms; Artificial intelligence; Cardiology; Cardiomyopathy; Computer and Information Sciences; Decision trees; Diagnosis; Diagnostic software; Diagnostic systems; Echo surveys; Echocardiography; EKG; Electrocardiography; Electrocardiography - methods; Engineering and Technology; Evaluation; Female; Health aspects; Heart; Heart hypertrophy; Hospitals; Humans; Hypertrophy; Hypertrophy, Left Ventricular - diagnosis; Learning algorithms; Machine Learning; Male; Medical diagnosis; Medicine and Health Sciences; Methods; Middle Aged; Model accuracy; Phenotypes; Physical Sciences; Power (Philosophy); Regression analysis; Regression models; Research and Analysis Methods; Risk groups; Sensitivity; Type 2 diabetes; Ventricle; Mexico; Netherlands
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0232657

The electrocardiogram (ECG) is the most common tool used to predict left ventricular hypertrophy (LVH). However, it is limited by its low accuracy (<60%) and sensitivity (30%). We set forth the hypothesis that the Machine Learning (ML) C5.0 algorithm could optimize the ECG in the prediction of LVH by echocardiography (Echo) while also establishing ECG-LVH phenotypes. We used Echo as the standard diagnostic tool to detect LVH and measured the ECG abnormalities found in Echo-LVH. We included 432 patients (power = 99%). Of these, 202 patients (46.7%) had Echo-LVH and 240 (55.6%) were males. We included a wide range of ventricular masses and Echo-LVH severities which were classified as mild (n = 77, 38.1%), moderate (n = 50, 24.7%) and severe (n = 75, 37.1%). Data was divided into a training/testing set (80%/20%) and we applied logistic regression analysis on the ECG measurements. The logistic regression model with the best ability to identify Echo-LVH was introduced into the C5.0 ML algorithm. We created multiple decision trees and selected the tree with the highest performance. The resultant five-level binary decision tree used only six predictive variables and had an accuracy of 71.4% (95%CI, 65.5-80.2), a sensitivity of 79.6%, specificity of 53%, positive predictive value of 66.6% and a negative predictive value of 69.3%. Internal validation reached a mean accuracy of 71.4% (64.4-78.5). Our results were reproduced in a second validation group and a similar diagnostic accuracy was obtained, 73.3% (95%CI, 65.5-80.2), sensitivity (81.6%), specificity (69.3%), positive predictive value (56.3%) and negative predictive value (88.6%). We calculated the Romhilt-Estes multilevel score and compared it to our model. The accuracy of the Romhilt-Estes system had an accuracy of 61.3% (CI95%, 56.5-65.9), a sensitivity of 23.2% and a specificity of 94.8% with similar results in the external validation group. In conclusion, the C5.0 ML algorithm surpassed the accuracy of current ECG criteria in the detection of Echo-LVH. Our new criteria hinge on ECG abnormalities that identify high-risk patients and provide some insight on electrogenesis in Echo-LVH.