Identification of wart treatment evaluation by using optimum ensemble based classification techniques

Warts caused by Human Papilloma Virus (HPV) do not disappear spontaneously with the body's natural immunity. This is due to many features of HPV, such as its weak immune effect, not entering the bloodstream and causing an immune response, and permanent infection of skin cells. HPV requires urgent treatment due to these characteristics and the fact that warts are most common on the hands and feet and are painful and disturbing. Choosing the most appropriate Cryotherapy or Immunotherapy treatment methods according to the patient's physiological characteristics is important in order to carry out the treatment process quickly. This study focuses on helping experts choose the most appropriate treatment for the most common plantar and common warts. For the selection of treatment method machine learning have been used. Bagged Tree and Subspace K-Nearest Neighbors (KNN) Ensemble classification algorithms were developed and tested on two data sets. These data sets were used to develop classification algorithms with pre- and post-treatment information of 180 patients receiving cryotherapy and immunotherapy treatments. The Subspace KNN Ensemble algorithm correctly predicted the treatment results of 88 of 90 patients receiving cryotherapy treatment, reaching 97.80% accuracy, and the Bagged Tree Ensemble algorithm correctly predicted 78 out of 90 patients receiving immunotherapy, reaching 86.7% accuracy. It has been shown that algorithms can help experts in terms of both high accuracy values ​​and fast decision making. The algorithms proposed have not been used before, and it has also been seen that they have superior performances than similar studies.