Radiomics Model Building and Model Evaluation
Four famous classifiers (KNN (k-nearest neighbor), support vector machine (SVM), random forest, and LightGBM) were used to create risk stratification models for radiological prediction after radiomics feature dimension reduction. Following that, a brief description of the four building methods mentioned previously. To begin, the k-Nearest Neighbor (KNN) classification algorithm, while theoretically mature, is also one of the most basic machine learning algorithms During the training phase, the hyperparameters of each classifier were tuned using an iterative grid search approach to prevent overfitting and to optimize the model's performance.Using the receiver operating characteristic (ROC) curve and calculating the area under the ROC curve, the prediction performance was evaluated (AUC). It was assessed, with the assistance of the calibration curves of the best combination model, whether the anticipated likelihood and the experimental findings were consistent with one another. This was accomplished by comparing the two sets of data. Decision curve analysis (DCA) was used to determine the clinical utility of the optimal combined model by calculating the net benefits for threshold probabilities.