A Hybrid Classification Example in the Diagnosis of Skin Disease with Cryotherapy and Immunotherapy Treatment

Uncontrolled tumors in the human body are called cancer. Unbalanced diet, alcohol and cigarette use, food additives and a variety of viruses can cause people have cancer. Cancer-causing tumors can be good or malignant. This study will measure the responses to treatments for skin disease caused by human papilloma virus (HPV), also called wart virus, which is directly related to cancer. This virus is an infectious virus that can infect another person by contact. There are multiple types of HPV virus and although it is usually benign, it can cause cancers such as cervical cancer, skin cancer. Apart from cancer, warts caused by HPV virus are generally seen on hands, feet, face and genital areas. As the skin grows and sagging progresses, it causes cancer at advanced levels. As a treatment method; drug use, surgical removal and HPV virus vaccination are used. These methods may require various surgical interventions. It can also cause a variety of reactions to allergic patients or it can cause a slight dependence on drug use. In addition to these methods, cryotherapy (ice treatment) and immunotherapy methods are used which are developed to obtain faster results and less costly than drugs and surgical interventions. In this study, it was estimated that 180 patients with warts on hands and feet who applied to the dermatology clinic of Ghaem Hospital in Iran were divided into two groups and responded to the treatment with two separate data sets obtained by applying cryotherapy in the other half and immunotherapy treatment in the other half. These data sets are located in the UCI data set. Navie Bayes, C4.5 decision tree, logistic regression, k- nearest neighbor classifier models have been developed for estimation work. In addition, the classification of the features included in the immunotherapy and cryotherapy data sets were tested by applying the feature selection process. The performance of the data sets after attribute selection and the performance of the raw data sets in the classification models are compared. 5 and 10 times cross validation is used to compare the performance of these models. The study also gave the best performance in all the performance criteria of the 4 different classifiers in the two datasets that are used as common models with the C4.5 Decision Tree. In addition, it is clearly seen that the attribute selection process has increased the performance criteria of all models.