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Liver Disease Detection Using Grey Wolf Optimization and Random Forest Classification

Liver Disease Detection Using Grey Wolf Optimization and Random Forest Classification
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Author(s): Singaravelan Shanmugasundaram (PSR Engineering College, India) and Parameswari M. (FX Engineering College, India)
Copyright: 2021
Pages: 31
Source title: Deep Learning Applications and Intelligent Decision Making in Engineering
Source Author(s)/Editor(s): Karthikrajan Senthilnathan (Revoltaxe India Pvt Ltd, Chennai, India), Balamurugan Shanmugam (Quants IS & CS, India), Dinesh Goyal (Poornima Institute of Engineering and Technology, India), Iyswarya Annapoorani (VIT University, India) and Ravi Samikannu (Botswana International University of Science and Technology, Botswana)
DOI: 10.4018/978-1-7998-2108-3.ch005

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Abstract

Utilizing machine learning approaches as non-obtrusive strategies is an elective technique in organizing perpetual liver infections for staying away from the downsides of biopsy. This chapter assesses diverse machine learning methods in expectation of cutting-edge fibrosis by joining the serum bio-markers and clinical data to build up the order models. An imminent accomplice of patients with incessant hepatitis C was separated into two sets—one classified as gentle to direct fibrosis (F0-F2) and the other ordered as cutting-edge fibrosis (F3-F4) as per METAVIR score. Grey wolf optimization, random forest classifier, and decision tree procedure models for cutting-edge fibrosis chance expectation were created. Recipient working trademark bend investigation was performed to assess the execution of the proposed models.

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