Fig. 4

Development of SVM models. a Selecting the optimal number of features (two features) using SVM-RFE in the training set. b Features were ranked according to the feature importance by SVM-RFE, and the top two features were selected for SVM model construction. c Selecting the optimal number of features (nine features) in the SMOTE-training set. d The top nine features were selected for SVM model construction. SVM: support vector machine; SVM-RFE: SVM-based recursive feature elimination; SMOTE: synthetic minority over-sampling technique