[Retracted] A Predictive Model for the Risk of Cognitive Impairment in Patients with Gallstones

Figure 2

Multiple model training prediction results are shown along with the selection process and prediction feature factor screening. (a) Line graph of the process of hyperparameter selection for the support vector machine model. The horizontal axis is the number of features incorporated into the support vector machine model, and the vertical axis is the accuracy of the corresponding model’s classification predictions. The model has the highest prediction accuracy in the training group when 10 features are included, with an accuracy of 0.747. (b) Based on the relationship between the model performance and the corresponding hyperparameters, the 10 features corresponding to the best model performance are included in the support vector machine model for training, and its classification prediction performance is evaluated in the test group. ; (c) the ROC curve shows that the random forest model has good classification performance in the test group (). (d) This bar graph shows the importance ranking of each factor incorporated in the random forest model modelling. Pittsburgh Sleep Quality Index (PSQI); Quality of Life Questionnaire Core 30 (QLQ-C30); (e) eight factors were obtained by single and multifactor logistic regression analysis. The ROC curve shows that the logistic regression prediction model constructed based on these eight factors had good classification prediction ability in both the training group () and the test group (0.767).

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