Open AccessTreatment Response Prediction in Hepatitis C Patients using Machine Learning Techniques
Author(s) -
Ashfaq Ali Kashif,
Birra Bakhtawar,
Asma Akhtar,
Samia Akhtar,
Nauman Aziz,
Muhammad Sheraz Javeid
Publication year - 2021
Publication title -
international journal of technology, innovation and management
Language(s) - English
Resource type - Journals
ISSN - 2789-777X
DOI - 10.54489/ijtim.v1i2.24
Subject(s) - random forest , machine learning , artificial intelligence , decision tree , naive bayes classifier , support vector machine , computer science , multilayer perceptron , artificial neural network , perceptron , drug naïve , ribavirin , medicine , hepatitis c virus , drug , virology , virus , pharmacology
The proper prognosis of treatment response is crucial in any medical therapy to reduce the effects of the disease and of the medication as well. The mortality rate due to hepatitis c virus (HCV) is high in Pakistan as well as all over the world. During the treatment of any disease, prediction of treatment response against any particular medicine is difficult. This paper focuses on predicting the treatment response of a drug: “L-ornithine L-Aspartate (LOLA)” in hepatitis c patients. We have used various machine learning techniques for the prediction of treatment response, including: “K Nearest Neighbor, kStar, Naive Bayes, Random Forest, Radial Basis Function, PART, Decision Tree, OneR, Support Vector Machine and Multi-Layer Perceptron”. Performance measures used to analyze the performance of used machine learning techniques include, “Accuracy, Recall, Precision, and F-Measure”.