Ensemble-Based Detection and Classification of Liver Diseases Caused by Hepatitis C

Document Type : Original Article

Authors

Faculty of Engineering & Technology, University of Mazandaran, Babolsar, Iran

Abstract

The liver, as the largest internal organ in the human body, plays a pivotal role in numerous physiological processes, orchestrating over 500 metabolic activities crucial for maintaining bodily functions. However, the Hepatitis C Virus (HCV) poses a grave threat to liver health, necessitating early identification of liver diseases to halt the progression to carcinoma and potentially save lives. This research aims to train ensemble-based algorithms for classifying and detecting Hepatitis, Fibrosis, and Cirrhosis. Employing rigorous preprocessing techniques, 80% of the dataset was allocated to train five ensemble-based algorithms: AdaBoost, Random Forest, Rotation Forest, XGBoost, and LightGBM. These algorithms were evaluated across four performance metrics—accuracy, precision, recall, and F1-score. Remarkably, LightGBM emerged as the frontrunner, boasting an exceptional accuracy rate of 98.37%. Rotation Forest followed closely with an accuracy of 96.74%, while XGBoost attained an accuracy of 95.12%. Random Forest and AdaBoost secured 94.19% and 93.30% accuracy, respectively. These findings underscore LightGBM’s prowess as a promising algorithm for detecting and classifying liver diseases. By leveraging advanced machine learning techniques, particularly ensemble-based algorithms, this research contributes to the ongoing efforts to enhance early detection, improve patient outcomes, and foster more effective management strategies for liver-related ailments in clinical settings

Keywords

References

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Contributions of Science and Technology for Engineering
Volume 1, Issue 1
March 2024
Pages 32-42

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History

  • Receive Date: 01 January 2024
  • Revise Date: 02 February 2024
  • Accept Date: 15 September 2024
  • First Publish Date: 15 September 2024
  • Publish Date: 15 March 2024

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