Open AccessMachine learning versus classic electrocardiographic criteria for the detection of echocardiographic left ventricular hypertrophy in a pre-participation cohort
Author(s) -
Daniel Yan Zheng Lim,
Gerald Sng,
Wilbert H. H. Ho,
Hankun Wang,
ChingHui Sia,
Joshua S.W. Lee,
Xiayan Shen,
Benjamin Yq Tan,
Edward C. Y. Lee,
Mayank Dalakoti,
Wang Kang Jie,
Clarence K.W. Kwan,
Weien Chow,
Ru San Tan,
Carolyn S.P. Lam,
Terrance Chua,
Tee Joo Yeo,
Daniel Chong
Publication year - 2021
Publication title -
kardiologia polska
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.527
H-Index - 34
eISSN - 1897-4279
pISSN - 0022-9032
DOI - 10.33963/kp.15955
Subject(s) - medicine , left ventricular hypertrophy , logistic regression , confidence interval , receiver operating characteristic , random forest , cardiology , machine learning , gradient boosting , artificial intelligence , area under the curve , population , odds ratio , blood pressure , computer science , environmental health
Classical electrocardiographic (ECG) criteria for left ventricular hypertrophy (LVH) are well studied in older populations and patients with hypertension. Their utility in young pre-participation cohorts is unclear.