Risk prediction for cardiovascular disease using ECG data in the China kadoorie biobank.
Journal:
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Published Date:
Aug 1, 2016
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.