The application of machine learning in predicting outcome of cryotherapy and immunotherapy for wart removal

Abstract

Background: Warts can be extremely painful conditions that may be associated with localised bleeding and discharge. They are commonly treated by cryotherapy or immunotherapy. However, each of these therapies have discomforting side effects and are no official dermatological guideline that exist that may be used to determine which of these methods would work on an individual patient. Objective: This study aimed at developing a machine learning algorithm that improved the prediction of the outcome of wart removing using cryotherapy and immunotherapy. Methods: Support vector machines, core vector machines, random forest, k-nearest neighbours, multilayer perceptron and binary logistic regression was applied on datasets in to create a model that predicted the outcome of an immunotherapy and cryotherapy treatments based on sex, age, time that has passed since last treatment, number of warts, type, area, diameter and result of treatment. Results: The average accuracy of the immunotherapy prediction was 88.6%±8.0% while the same measure for cryotherapy prediction was 94.6%±4.0%. The most efficient immunotherapy and cryotherapy model had an accuracy of 100%, predicating the correct treatment outcome when applied to all test cases. Conclusion: This study successfully created a machine learning model that improved the prediction ability of the outcome of immunotherapy and cryotherapy for wart removal. This model created a more in-depth guideline for understanding is immunotherapy would work and took a new approach to cryotherapy.