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Warts are produced on the human body because of contamination caused by Human Papillomavirus (HPV). The most affected areas of warts are hands and feet specifically, which is bit annoying and hard to recover in later stages. After massive literature survey, it is found that different treatments have been suggested for treating this illness. The basic problem found while treating that the treatment given to one patient may or may not be suitable for another patient, so it is hard to detect specific kind of treatment to be followed for faster recovery and effective customized treatment against this viral disease. The objective of this research work is to identify the ideal treatment method for both particular plantar and normal warts, between immunotherapy and cryotherapy treatment methods. Implications of machine learning techniques are now playing a vital role specifically in clinical diagnosis toward identifying different clinical patterns, disease classification and its predictions. In this research, work authors have implemented classifiers like Bayes Net, SVM, Multi-Layer Perceptron, k-NN, FURIA, Random Forest with the help of WEKA tool. The experimentation has been performed on data sets obtained from UCI Machine Learning Repositories. The experimentation was performed with total 180 patient instances having wart illness present in immunotherapy and cryotherapy datasets respectively. The result outcomes have been discussed and compared with existing methodologies mentioned in the literature. It was observed that the decision tree based classifier random forest is having the best classification accuracy among the chosen set of classifiers. The result shows highest classification accuracy in case random forest, 86% and 93% was noted for immunotherapy and cryotherapy treatment methods datasets. This research work is helpful for physicians in selecting the best treatment method for their patient suffering from wart illness in order to reduce overall treatment cost incurred and also improving the quality of treatment given to the patients.

Implicating Machine Learning towards Development of Intelligent System for Wart Treatment Therapy Identification