. To prevent measurement bias, ECG features were manually reviewed to identify erroneous calculations. Physiologically plausible outliers were replaced with ±3 s.d. On average, each feature had a 0.34% missingness rate . Thus, we imputed missing values with the mean, median or mode of that feature after consultation with clinical experts. ECG metrics were then-score normalized and used as input features in machine learning models.
The RF classifier achieved high accuracy on the training set with a relatively small drop in performance on the test set , indicating an acceptable bias–variance tradeoff and low risk of overfitting . Although the SVM model had lower variance on the test set, when compared to the RF model there were no significant differences in AUROC or their binary classifications .
All diagnostic accuracy values were reported as per Standards for Reporting Diagnostic Accuracy Studies recommendations. We reported classification performance using AUROC curve, sensitivity , specificity, PPV and NPV, along with 95% CI where applicable. For 10-fold cross validation, we compared the multiple classifiers using the Wilcoxon signed-rank test and McNemar’s test .
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