Prognostic prediction models for postoperative patients with stage I to III colorectal cancer based on machine learning

Figure 1 Flowchart showing colorectal cancer patient cohort selection and model training and performance evaluation. A total of 1330 patients were recruited for model development and were grouped by oncological outcomes; then, the data were randomly divided at a 7:3 ratio into a training set and a testing set. The hyperparameters were determined for both the training set and testing set, and model performance was evaluated on the basis of the area under the receiver operating characteristic curve. Finally, we predicted patient outcomes, screened important variables, and processed the bootstrap iterations. DFS: Disease-free survival; DMFS: Distant metastasis-free survival; OS: Overall survival; RFS: Recurrence-free survival.