Hepatitis C prediction using SVM, logistic regression and decision tree
1 Management Information Systems, College of Business, Lamar University, Beaumont, TX 77710, USA.
2 Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO 65401, USA.
2 Electrical and Computer Engineering, Altinbas University, Istanbul, Turkey.
3 Department of Electrical Engineering, School of Engineering, San Francisco Bay University, Fremont, CA 94539, USA.
4 Medical Assistant Training School, Bagerhat, Bangladesh.
5 Bandhan Private Hospital, Faridpur, Bangladesh.
Research Article
World Journal of Advanced Research and Reviews, 2024, 22(02), 926–936
Publication history:
Received on 03 April 2024; revised on 11 May 2024; accepted on 13 May 2024
Abstract:
Hepatitis C is an infection of the liver brought on by the HCV virus. In this condition, early diagnosis is challenging because of the delayed onset of symptoms. Predicting well enough can spare patients from permeant liver damage. The primary goal of this work is to use several machine learning methods to forecast this disease based on widely available and reasonably priced blood test data in order to diagnose and treat patients early on. Three machine learning techniques support vector machine (SVM), logistic regression, decision tree, has been applied on one dataset in this work. To find a suitable approach for illness prediction, the confusion matrix, precision, recall, F1 score, accuracy, receiver operating characteristics (ROC), performances of different strategies have been assessed. The SVM model's overall accuracy is 0.92, the highest among the three models.
Keywords:
Machine Learning Techniques; Hepatitis C Virus; Data Mining; Decision Tree; HCV; Performance Measurements; ROC.
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Copyright information:
Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0