Treatment Response Prediction in Hepatitis C Patients using Machine Learning Techniques

Authors

  • Ashfaq Ali Kashif Virtual University of Pakistan
  • Birra Bakhtawar Virtual University of Pakistan
  • Asma Akhtar Virtual University of Pakistan
  • Samia Akhtar Virtual University of Pakistan
  • Nauman Aziz Superior University, Lahore, Pakistan
  • Muhammad Sheraz Javeid National College of Business Administration & Economics, Lahore, Pakistan

DOI:

https://doi.org/10.54489/ijtim.v1i2.24

Abstract

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”.

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Published

2021-12-07

How to Cite

[1]
“Treatment Response Prediction in Hepatitis C Patients using Machine Learning Techniques”, Int. J. TIM, vol. 1, no. 2, pp. 79–89, Dec. 2021, doi: 10.54489/ijtim.v1i2.24.