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. 2022 Apr 27:10:892371.
doi: 10.3389/fpubh.2022.892371. eCollection 2022.

Artificial Intelligence-Based Ensemble Learning Model for Prediction of Hepatitis C Disease

Michael Onyema Edeh  1 , Surjeet Dalal  2 , Imed Ben Dhaou  3 , Charles Chuka Agubosim  4 , Chukwudum Collins Umoke  5 , Nneka Ernestina Richard-Nnabu  6 , Neeraj Dahiya  7
Affiliations

Artificial Intelligence-Based Ensemble Learning Model for Prediction of Hepatitis C Disease

Michael Onyema Edeh et al. Front Public Health. .

Abstract

Machine learning algorithms are excellent techniques to develop prediction models to enhance response and efficiency in the health sector. It is the greatest approach to avoid the spread of hepatitis C, especially injecting drugs, is to avoid these behaviors. Treatments for hepatitis C can cure most patients within 8 to 12 weeks, so being tested is critical. After examining multiple types of machine learning approaches to construct the classification models, we built an AI-based ensemble model for predicting Hepatitis C disease in patients with the capacity to predict advanced fibrosis by integrating clinical data and blood biomarkers. The dataset included a variety of factors related to Hepatitis C disease. The training data set was subjected to three machine-learning approaches and the validated data was then used to evaluate the ensemble learning-based prediction model. The results demonstrated that the proposed ensemble learning model has been observed ad more accurate compared to the existing Machine learning algorithms. The Multi-layer perceptron (MLP) technique was the most precise learning approach (94.1% accuracy). The Bayesian network was the second-most accurate learning algorithm (94.47% accuracy). The accuracy improved to the level of 95.59%. Hepatitis C has a significant frequency globally, and the disease's development can result in irreparable damage to the liver, as well as death. As a result, utilizing AI-based ensemble learning model for its prediction is advantageous in curbing the risks and improving treatment outcome. The study demonstrated that the use of ensemble model presents more precision or accuracy in predicting Hepatitis C disease instead of using individual algorithms. It also shows how an AI-based ensemble model could be used to diagnose Hepatitis C disease with greater accuracy.

Keywords: Quest; artificial intelligence; ensemble learning; hepatitis C; machine learning.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
MLP Diagram.
Figure 2
Figure 2
MLP Accuracy.
Figure 3
Figure 3
MLP Classification for Category.
Figure 4
Figure 4
Bayesian network for current problem.
Figure 5
Figure 5
Bayesian network Accuracy.
Figure 6
Figure 6
Bayesian network Classification for Category.
Figure 7
Figure 7
QUEST for current problem.
Figure 8
Figure 8
Quest Classification for Category.
Figure 9
Figure 9
Proposed Ensemble learning model.
Figure 10
Figure 10
Accuracy level of Proposed Ensemble learning model.
Figure 11
Figure 11
Accuracy comparison.
See this image and copyright information in PMC

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