Human Papillomavirus Targeted Immunotherapy Outcome Prediction Using Machine Learning

Abstract

Warts caused by the Human Papillomavirus (HPV) is a highly contagious disease, and affects several million people across the globe every year, in the form of small lesions on the skin, commonly known as warts. Warts can be treated effectively with several methods, the most effective being Immunotherapy and Cryotherapy. Our research is focused on the performance comparison of modern Machine Learning classification techniques to predict the outcome (positive or negative) of Immunotherapy treatment given to a patient, by using patient data as input features to our classifiers. The precision, recall, f-measure and accuracy were used to compare the performance of the various classifiers considered in this study. We considered Logistic Regression, ZeroR, AdaBoost, K-Nearest Neighbours (KNN), Support Vector Machines (SVM), Gradient Boosting, Repeated Incremental Pruning to Produce Error Reduction (RIPPER), Decision Trees and Random Forests. The ZeroR classifier was used as a baseline to provide us with insights into the skewed nature of the data, so as to enable us to better understand the comparison in performance of the various classifiers.

Automatic Classification of the Cerebral Vascular Bifurcations Using Dimensionality Reduction and Machine Learning

“…And finally, XGBoost is a popular supervised machine learning algorithm used for classification and prediction problems. It is a modified version of the gradient boosting algorithm, which classifies data by combining model resulting from classification decision trees [39]. In the context of our work, the best algorithm was selected by cross-validation, as described within the next section, based on the evaluation metrics.…”