AUTHOR=Edeh Michael Onyema , Dalal Surjeet , Dhaou Imed Ben , Agubosim Charles Chuka , Umoke Chukwudum Collins , Richard-Nnabu Nneka Ernestina , Dahiya Neeraj 

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

JOURNAL=Frontiers in Public Health

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2022.892371

DOI=10.3389/fpubh.2022.892371

ISSN=2296-2565

ABSTRACT=Machine learning algorithms are excellent techniques to develop prediction models to enhance response and efficiency in the health sector. Due to the non-linear nature of disease development, clinical risk prediction models in chronic hepatitis C disease can be difficult. 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 proved to be 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.