The construction of RF and logistic regression model
tumor
REGRESSION, EVENT, METASTASIS, TUMOR
In this study, we first established an RF model in the 1-year cohort, and the outcome variable was whether the patients had an event (defined as tumor relapse or metastasis). Based on the result of the chi-square test and the Cox regression, multiple factors that might influence the outcomes were chosen as explanatory variables, which were used to classify and predict the outcome variables. The data were randomly and independently divided into the training set and the test set according to the ratio of 8:2.Then, we used the training set for developing optimal models. The importance of each explanatory variable was assessed by evaluating the Mean decrease accuracy and the Mean decrease gini. The higher the value, which means the more important the explanatory variable in the model. The RF model was tested in the test set, and the receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used to measure the accuracy of the models.Next, the same explanatory variables in the RF model were used to develop a logistic regression to classify and predict the outcome variable in the 1-year cohort. Finally, with the same method as the above mentioned, an RF model and a logistic regression were developed in the 5-year cohort. The performance was compared between the RF model and the logistic regression based on specificity, sensitivity, and AUC.