Predicting the success of wart treatment methods using decision tree based fuzzy informative images

Warts are small, rough, benign tumours caused by human papillomavirus (HPV). A challenge is predicting the success of wart treatment methods because success may vary depending on the patient and the features of disease. Recently, a machine learning based expert prediction system and related prediction rules were proposed. However, the success of this system is not satisfactory and should be improved. Furthermore, medical experts find it difficult to interpret the suggested rules of this system. The decision tree-based method was accordingly used in this study to determine the rules of predicting the success of wart treatment methods. According to findings, the success rate varied from 90 to 95% according to the treatment method; these rates are higher than previously reported. Furthermore, the decision tree rules that were determined can be transformed into images to visually interpret the success rates of treatment methods as a function of patient age and the time elapsed since disease appearance. This study provides a method for simple and more accurate interpretation of rules for medical experts. The success of treatment methods is now predictable as a percentage.