Before revision:  Finally, in Phase III, a ten-fold cross-validation method is used to compare the performance of seven machine-learning algorithms for classification . Results obtained with SMOTE-Multilayer Perceptron with GA-based 14 features show the highest classification accuracy ( 0.98 ), a sensitivity of 0.99, and a specificity of 0.98, outperforming other Spitzoid lesionsclassification algorithms .
After revision:  Finally, in Phase III, a ten-fold cross-validation method is used to compare the performance of seven machine-learning algorithms for classification . Results obtained with SMOTE-Multilayer Perceptron with GA-based 14 features show the highest classification accuracy ( 0.98 , AUC 0.98, and G-mean 0.97.Results obtained in this study have potential to open new opportunities in diagnosis of spitzoid lesions .