Supplementary Materials
tumor, NB
REGRESSION, COLORECTAL CANCER, TUMOR
Analytical diagram of the percentage of the situation within each variable. We performed a statistical analysis of the individual signs of the included patients, which is presented in the form of a bar chart that clearly shows the proportion of the number of patients for each variable.Click here for additional data file.Display of variable coefficients in logistic regression model. For the logistic regression model, the coefficients assigned to each feature were recorded.Click here for additional data file.LASSO regression feature filtering. LASSO (least absolute shrinkage and selection operator) regression based on five times tenfold cross-validation was used for feature selection.Click here for additional data file.Patient screening process. The detailed process of patient selection.Colinearity analysis. Variables exhibiting colinearity were excluded from variate analysis. The darker blue color indicates higher colinearity.LASSO regression feature filtering. LASSO (least absolute shrinkage and selection operator) regression based on five times tenfold cross-validation was used for feature selection.Random forest feature filtering. Random forest based on five times tenfold cross-validation was used to perform feature selection.Receiver operating characteristic (ROC) curves of predictive models. Diagnostic abilities of predictive models for the differential diagnosis of dMMR and pMMR in the test set. ROC curves of predictive model created by LR, NB, RF, SVM, and XGBoost.Significance analysis by LASSO regression. For the LASSO (least absolute shrinkage and selection operator) regression, we give the normalized regression coefficients for each feature.Significance analysis by Random forest. We used the machine learning technique and random forest, to determine feature importance.Variable importance analysis. The merged variables were performed for feature importance analysis by random forest.Clinical characteristics of the patients with colorectal cancer.Risk factors for deficient MMR in colorectal cancer. Five predictive models based on simplified clinicopathological features and serum tumor biomarkers.
Performance of different predictive models to identify dMMR. Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of five predictive models in the test set.