Clinically interpretable electrovectorcardiographic machine learning criteria for the detection of echocardiographic left ventricular hypertrophy

Fernando De la Garza-Salazar and Brian Egenriether

PLOS ONE, 2025, vol. 20, issue 10, 1-20

Abstract: Echocardiographic left ventricular hypertrophy (Echo-LVH) is frequently underdetected by traditional electrocardiogram (ECG) criteria due to limited sensitivity. We investigated whether integrating ECG with vectorcardiography (VCG) using a clinically interpretable machine learning algorithm (C5.0) could improve diagnostic performance. We analyzed ECG and VCG data from 664 patients, 42.8% of whom had Echo-LVH. The study introduced three new criteria—Marcos VCG, Marcos VCG-ECG, and Marcos VCG-ECGsp—named in honor of the software used for VCG synthesis, and compared their diagnostic performance against 23 established ECG criteria, including Cornell voltage, Peguero-Lo Presti, and Sokolow-Lyon. Marcos VCG-ECGsp, optimized for higher specificity, was included to evaluate trade-offs in performance. Validation was performed using train/test split and 10-fold cross-validation. Marcos VCG-ECG achieved higher AUC than Cornell voltage in both training (0.81 vs. 0.68, p

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0334829

DOI: 10.1371/journal.pone.0334829

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