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Diagnosing the Stage of Hepatitis C Using Machine Learning.

Muhammad Bilal Butt1, Majed Alfayad2, Shazia Saqib1

  • 1Department of Computer Science, Lahore Garrison University, Lahore 54000, Pakistan.

Journal of Healthcare Engineering
|January 14, 2022
PubMed
Summary

This study introduces an Intelligent Hepatitis C Stage Diagnosis System (IHSDS) using Artificial Neural Network (ANN) for accurate Hepatitis C staging. The IHSDS achieved high precision, offering a non-invasive alternative to liver biopsy for disease management.

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Area of Science:

  • Hepatology
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Hepatitis C is a global health concern with millions of new cases annually.
  • Accurate staging of Hepatitis C is crucial for effective patient management and treatment.
  • Traditional methods for staging can be invasive, necessitating alternative approaches.

Purpose of the Study:

  • To develop and evaluate an Intelligent Hepatitis C Stage Diagnosis System (IHSDS).
  • To utilize machine learning, specifically Artificial Neural Network (ANN), for non-invasive Hepatitis C staging.
  • To compare the performance of the proposed system with existing models.

Main Methods:

  • A dataset with 29 features was sourced from the UCI machine learning repository.
  • 19 relevant features were selected for the study.
  • The dataset was split into 70% for training and 30% for validation.
  • An Artificial Neural Network (ANN) model was implemented within the IHSDS.

Main Results:

  • The IHSDS demonstrated high predictive accuracy.
  • Achieved 98.89% precision during the training phase.
  • Achieved 94.44% precision during the validation phase.

Conclusions:

  • The IHSDS provides a precise and non-invasive method for Hepatitis C staging.
  • Machine learning, particularly ANN, is a viable tool for diagnosing liver disease stages.
  • The developed system shows significant potential for clinical application in Hepatitis C management.