Objective To explore the predictive efficacy of three machine learning algorithms for carotid plaque stability in patients with cerebral infarction.

Methods The clinical data of 500 patients with cerebral infarction were retrospectively analyzed. Univariate analysis and multivariate analysis were used to determine the predictive factors entering the model. The prediction model of carotid plaque stability in patients with cerebral infarction was constructed based on nomogram, decision tree and random forest respectively. The enrolled patients were randomly divided into training set and test set according to the ratio of 7:3. Sensitivity, specificity, accuracy, recall, accuracy and area under the curve (AUC) were used to compare the application efficiency of the model.

Results The AUC of the nomogram model for evaluating the stability of carotid plaque in patients with cerebral infarction in the training set was 0.910(95%CI, 0.950 to 0.983), the sensitivity was 0.910, the specificity was 0.917, the accuracy was 0.886, the recall rate was 0.910, and the accuracy rate was 0.914. The AUC of the decision tree model for evaluating the stability of carotid plaque in patients with cerebral infarction in the training set was 0.932(95%CI, 0.903 to 0.961), the sensitivity was 0.903, the specificity was 0.922, the accuracy was 0.891, the recall rate was 0.903, and the accuracy rate was 0.914. The AUC of the random forest model for evaluating the stability of carotid plaque in patients with cerebral infarction in the training set was 0.984(95%CI, 0.970 to 0.998), the sensitivity was 0.972, the specificity was 0.995, the accuracy was 0.993, the recall rate was 0.972, and the accuracy was 0.986.

Conclusion The model based on the random forest algorithm has a better prediction effect and stability in evaluating the stability of carotid plaque in patients with cerebral infarction, and its prediction efficiency is better than that of the Nomogram and decision tree.