Construction and performance validations of machine learning models
REGRESSION, POSITIVE
Based on the predictive factors, 6 machine learning models were constructed. The AUC value in the training set of the GBDT model was 0.994 [95% confidence interval (CI): 0.988 to 1.000], higher than the AUC value of the LR model (0.890, 95% CI: 0.860 to 0.920), AdaBoost model (0.918, 95% CI: 0.894 to 0.941), SVM model (0.912, 95% CI: 0.888 to 0.936), KNN model (0.908, 95% CI: 0.883 to 0.933), and MLP model (0.948, 95% CI: 0.929 to 0.967). In the testing set, GBDT had the highest AUC value (0.985, 95% CI: 0.966 to 1.000), thereby, GBDT was selected as the final predictive model. The ACU of the GBDT model (0.985, 95% CI: 0.966 to 1.000) was higher than the LR model (0.896, 95% CI: 0.841 to 0.951), achieving statistical significance (
Construction and performance validations of machine learning modelsNotes: SVM: Support vector machine; KNN: K-nearest neighbor; MLP: multi-layer perceptron; LR: logistic regression; GBDT: gradient boosting decision tree; AdaBoost: adaptive enhancement algorithm; PPV: Positive predictive values; NPV: Negative predictive values; AUC: Area under curve; CI: confidence interval; Ref: Reference