Use of Support Vector Machine(SVM) in Prediction of Success of Wart Treatment Methods

• Published in: International journal for research in applied science and engineering technology Vol. 11; no. 12; pp. 1040 - 1049
• Main Authors: Reddy, R Damodar, Deekshitha, S, Reddy, A Deepika, Bhat, B S Varun, Reddy, K Dhakshinya, Dhanush, R
• Format: Journal Article
• Language: English
• Published: 31.12.2023
• ISSN: 2321-9653; 2321-9653
• DOI: 10.22214/ijraset.2023.57513

Warts are common noncancerous benign tumors caused by the Human Papilloma Virus. They can be treated using various methods, including cryotherapy and immunotherapy. However, the success rates of these treatment methods are not consistent and can vary from patient to patient. To address this issue, we have developed a reliable machine learning model that can accurately predict the success of immunotherapy and cryotherapy for individual patients based on their demographic and clinical characteristics. By utilizing a dataset of 180 patients who received either immunotherapy or cryotherapy for their warts, we employed a support vector machine classifier with a radial basis function kernel for the immunotherapy treatment method (Nugroho et al., 2018) . This model takes into account factors such as sex, age, time, number of warts, type, area, and response to treatment to make predictions about the likelihood of treatment success. Through this model, healthcare professionals can have a better understanding of which treatment method is more likely to be effective for a specific patient. The accuracy of our machine learning model for predicting the success of immunotherapy and cryotherapy for warts is expected to be high, as previous studies using similar datasets have reported classification accuracies of up to 90 (Rahman et al., 2020). Our model builds upon previous research in the field, such as the fuzzy logic rule-based method developed by Khozeimeh et al and the AdaBoost with classification and regression tree and random forest algorithms employed by Putra et al. Using our machine learning model, healthcare professionals can make more informed decisions about the treatment approach for individual patients with warts, increasing the likelihood of successful outcomes and improving overall patient care. In conclusion, our machine learning model offers a promising approach to predict the success of immunotherapy and cryotherapy treatments for warts.