Figure 1
Workflow for data management and myocardial ischemia prediction model development. Data came from 4000 adults who completed physical exams at the 920th Hospital of Joint Logistics Support Force between January and June 2022 and prepared to enter the plateau within 6 months. After exclusion, there were 2855 people remaining. Participants' data (n = 2855) were randomly assigned to a prediction model training dataset (n = 2141) and a test dataset (n = 714) in a 3:1 ratio following preprocessing. fivefold cross-validation was used for training and selecting the prediction model, and five classification algorithms were evaluated. Feature selection was conducted using the RFE (Recursive Feature Elimination) algorithm. The final prediction model was validated using a test dataset. LR logistic regression, RF random forest, XGBoost eXtreme gradient boosting, KNN K-nearest neighbor, SVM support vector machines.
