Prediction of wart treatment response using a hybrid GA-ensemble learning approach

Warts are a common type of skin disease that is caused mainly by human papillomavirus (HPV). Several ap-proaches are used to treat warts, which include the popular cryotherapy and immunotherapy methods. Various machine learning-based approaches have been introduced to help physicians select a proper treatment method for each patient. The main drawback of those approaches is the limitation of samples used for each of those models (90 samples for cryotherapy model and 90 samples for immunotherapy model). This study develops a reliable wart treatment prediction system using a hybrid genetic algorithm (GA)-ensemble learning approach with a larger dataset. The immunotherapy and cryotherapy datasets used in previous studies are separately balanced and then combined into one wart dataset, and the treatment method is modeled as one of the input features. GA is combined with learning algorithms, namely backpropagation neural networks (BPNN), support vector machine (SVM), classification and regression tree (CART), and K-nearest neighbors (KNN), to determine the optimal features for the prediction model. The four base classifiers, GA-BPNN, GA-SVM, GA-CART, and GA-KNN, are ensembled using bagging, boosting, and stacking techniques. The results show that ensemble models yield better classification results on the combined balanced wart dataset than individual classifiers. In particular, the stacking model with GA-SVM, GA-CART, and GA-KNN as layer-1 classifiers performs the best (accuracies of 100 % and 98.3 % on 10-fold cross validation and testing samples, respectively). Furthermore, the stacking model outperforms the model introduced in the literature on the same combined dataset with 180 samples (97.2 % average accuracy of 10-fold cross validation compared to 89.3 %). The proposed prediction system can assist physicians to reliably select appropriate treatment methods for patients who have warts with high accuracy, which in turn will have a positive impact on clinical resources.