Open Access

ARTICLE

Fuzzy Logic-Based System for Liver Fibrosis Disease

by Tamim Alkhalifah^1,*, Jimmy Singla², Fahad Alurise¹

1 Department of Computer, College of Science and Arts in Ar Rass, Qassim University, Ar Rass, Qassim, 52571, Saudi Arabia
2 Department of Computer Science and Engineering, Lovely Professional University, Phagwara 144001, India

* Corresponding Author: Tamim Alkhalifah. Email:

Computer Systems Science and Engineering 2023, 46(3), 3559-3582. https://doi.org/10.32604/csse.2023.036534

Received 03 October 2022; Accepted 13 January 2023; Issue published 03 April 2023

Abstract

The diagnosis of liver fibrosis (LF) is crucial as it is a deadly and life-threatening disease. Artificial intelligence techniques aid doctors by using the previous data on health and making a diagnostic system, which helps to take decisions about patients’ health as experts can. The historical data of a patient’s health can have vagueness, inaccurate, and can also have missing values. The fuzzy logic theory can deal with these issues in the dataset. In this paper, a multilayer fuzzy expert system is developed to diagnose LF. The model is created by using multiple layers of the fuzzy logic approach. This system aids in classifying the health of patients into different classes. The proposed method has two layers, i.e., layer 1 and layer 2. The input variables used in layer 1 for diagnosing liver fibrosis are Appetite, Jaundice, Ascites, Age, and Fatigue. Similarly, in layer 2, the input variables are Platelet count, White blood cell count, spleen, SGPT ALT (Serum Glutamic Pyruvic Transaminase Alanine Aminotransferase), SGOT ALT (Serum Glutamic-oxalacetic Transaminase Alanine Aminotransferase), Serum bilirubin, and Serum albumin. The output variables for this developed system are no damage, minimal damage, significant damage, severe damage, and cirrhosis. This research work also presents the examination of results based on performance parameters. The proposed system achieves a classification accuracy of 95%. Moreover, other performance parameters such as sensitivity, specificity, and precision are calculated as 97.14%, 92%, and 94.44%, respectively.

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Keywords

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