DOIONLINE

DOIONLINE NO - IJAECS-IRAJ-DOIONLINE-19394

Publish In
International Journal of Advances in Electronics and Computer Science-IJAECS
Journal Home
Volume Issue
Issue
Volume-10,Issue-1  ( Jan, 2023 )
Paper Title
Prediction of Antiviral Treatment Response of Hepatitis B Egyptian Patients using Machine Learning
Author Name
Eslam Taher Sharshar, Huda Amin Maghawry, Eman Abdelsameea Mahmoud, Nagwa Badr
Affilition
1,2,4Information Systems Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt 3Hepatology and Gastroenterology Department, National Liver Institute, Menoufia University, Menoufia, Egypt
Pages
33-39
Abstract
Abstract - Problem: Knowing the patient’s response to treatment is a very important issue. Therefore, it is necessary to predict the treatment response to know the effect of drugs. Recently, many machine learning techniques were applied for treatment response prediction. Methods: The aim of this study is to efficiently predict the antiviral treatment response of Hepatitis B Egyptian patients. This was achieved by applying multiple machine learning techniques, which are Decision Tree, Random Forest, k-Nearest Neighbor, Gradient Boosting. The input features include clinical laboratory features, quantitative level for Hepatitis B virus (DNAHBV) plus fibrosis stage and the antiviral drug type (Entecavir, Tenofovir and Lamivudine). Also, two over-sampling techniques were applied to overcome the data imbalance issues. They are Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN). Attribute selection method was also applied to reduce the dimensionality of features. Results: The highest accuracies achieved by Decision Tree, Random Forest, k-Nearest Neighbor, Gradient Boosting were 85.7%, 85.7%, 92.9% and 85.7% respectively. Conclusion: The best classification model for antiviral treatment response on HBV Egyptian patients was kNN classifier model. It achieved an accuracy of 92.9%, recall of 1.0 for response class and 0.75 for non-response class, and precision of 0.91 for response class and 1.0 for non-response class. Keywords - Machine-Learning; Treatment Response Prediction; Hepatitis B Virus Egyptian Patients; Decision Tree; Random Forest; K nearest Neighbor; Gradient Boosting.
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