title: Risk prediction for cardiovascular disease using ECG data in the China kadoorie biobank.
abstract: We set out to use machine learning techniques to analyse ECG data to improve risk evaluation of cardiovascular disease in a very large cohort study of the Chinese population. We performed this investigation by (i) detecting "abnormality" using 3 one-class classification methods, and (ii) predicting probabilities of "normality", arrhythmia, ischemia, and hypertrophy using a multiclass approach. For one-class classification, we considered 5 possible definitions for "normality" and used 10 automatically-extracted ECG features along with 4 blood pressure features. The one-class approach was able to identify abnormality with area-under-curve (AUC) 0.83, and with 75.6% accuracy. For four-class classification, we used 86 features in total, with 72 additional features extracted from the ECG. Accuracy for this four-class classifier reached 75.1%. The methods demonstrated proof-of-principle that cardiac abnormality can be detected using machine learning in a large cohort study.
authors.lastname: Yanting Shen
authors.lastname: Yang Yang
authors.forename: Sarah
authors.initials: S
authors.lastname: Parish
authors.lastname: Zhengming Chen
authors.forename: Robert
authors.initials: R
authors.lastname: Clarke
authors.forename: David A
authors.initials: DA
authors.lastname: Clifton
mesh_terms: D019540:Area Under Curve; D001145:Arrhythmias, Cardiac / Q000175:diagnosis* / Q000453:epidemiology; D018070:Biological Specimen Banks; D001794:Blood Pressure; D002318:Cardiovascular Diseases / Q000175:diagnosis* / Q000453:epidemiology; D002681:China; D015331:Cohort Studies; D016208:Databases, Factual; D004562:Electrocardiography; D006801:Humans; D000069550:Machine Learning; D011336:Probability; D012044:Regression Analysis; D015203:Reproducibility of Results; D018570:Risk Assessment