Before revision:  In Phase I, collected data are preprocessed with an effective Synthetic Minority Oversampling TEchnique or SMOTE-based method being implemented to treat the imbalance data problem . Then, a feature selection mechanism using genetic algorithm (GA) is applied in Phase II. 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:  In Phase I, collected data are preprocessed with an effective Synthetic Minority Oversampling TEchnique or SMOTE-based method being implemented to treat the imbalance data problem , in the second stage, in order to eliminate irrelevant features; genetic algorithm 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 .