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Compared to support vector machine, the model based on the random forest algorithm showed better accuracy (93.1%, 95% confidence interval [CI] 0.913-0.95, precision (92.4%, 95% CI 0.897-0.951), F1 score (91.5%, 95% CI 0.889-0.964), and recall score (93.6%, 95% CI 0.909-0.964), and yielded higher area under the receiver operating characteristic curve (AU-ROC) (0.962, 95% CI 0.942-0.982). The constructed models exhibit good prediction accuracy and efficiency. It might be used in clinical practice to facilitate target intervention for ac
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