Use of various machine learning algorithms such as KNN and ANN to predict the accuracy of Cryotherapy treatment on patients with wart disease under the guidance of Prof. Divya Kumari.
We are profoundly grateful to Prof. Divya Kumari for her expert guidance and continuous encouragement throughout to see that this project rights its target since its commencement to its completion. We would like to express our thanks and appreciation to our esteemed institution, KIIT deemed to be university, for giving us the opportunity to develop the project.
As benign tumors, warts are made through the mediation of Human Papillomavirus (HPV) and may grow on all parts of body, especially hands and feet. There are several treatment methods for this illness. However, none of them can heal all patients. Consequently, physicians are looking for more effective and customized treatments for each patient. They are endeavoring to discover which treatments have better impacts on a particular patient. The aim of this study is to identify the appropriate treatment for two common types of warts (plantar and common) and to predict the responses of two of the best methods (immunotherapy and cryotherapy) to the treatment. The study on which this project is based was conducted on 180 patients, with plantar and common warts, who had referred to the dermatology clinic of Ghaem Hospital, Mashhad, Iran. In this study, 90 patients were treated by cryotherapy method with liquid nitrogen and 90 patients with immunotherapy method. The selection of the treatment method was made randomly. A fuzzy logic rule-based system was proposed and implemented to predict the responses to the treatment method. It was observed that the prediction accuracy of immunotherapy and cryotherapy methods was 83.33% and 80.7%, respectively. The aim of this project is to improve this accuracy using machine learning algorithms. According to the results obtained, the benefits of this expert system are multifold: assisting physicians in selecting the best treatment method, saving time for patients, reducing the treatment cost, and improving the quality of treatment.