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

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.

Opportunities and challenges for the application of artificial intelligence paradigms into the management of endemic viral infections: The example of Chronic Hepatitis C Virus

“…Despite being a highly recommended technique to obtain more reliable results in terms of overfitting and generalisability, the four models 39,43,51,56 did not run an external validation 106 …”

“…Ten laboratory variables: albumin, alkaline phosphatase, alanine transaminase (ALT), aspartate transaminase (AST), bilirubin, acetylcholinesterase, cholesterol, creatinine, gamma-glutamyl transferase (GGT), and plasma proteins for 615 patients in addition to age and sex, were used by Edeh 43 to predict HCV infection and liver cirrhosis.…”

“…The preclinical fields involved are the development of novel HCV treatments and HCV/human immunodeficiency virus-1 (HIV-1) coinhibitors, [34][35][36][37][38] while the clinical aspects covered are the early detection of HCV infection, [39][40][41][42][43] the prediction of DAAs treatment outcomes. [44][45][46][47][48][49][50] The development of HCV vaccines, 51 and the early diagnosis of HCV-related HCC.…”

“…104,105 The Net-Benefit value can be calculated using the true positives, the false positives, and a clinical predefined threshold for opting for the intervention/treatment of interest (Equation S2.3). 33 Despite being a highly recommended technique to obtain more reliable results in terms of overfitting and generalisability, the four models 39,43,51,56 did not run an external validation. 106 Added to the prediction functionality, many of the introduced applications employed several AI/ML-inherited dimensionality reduction techniques to identify the key determinants of the inspected outcome.…”

“…Ten laboratory variables: albumin, alkaline phosphatase, alanine transaminase (ALT), aspartate transaminase (AST), bilirubin, acetylcholinesterase, cholesterol, creatinine, gamma‐glutamyl transferase (GGT), and plasma proteins for 615 patients in addition to age and sex, were used by Edeh 43 to predict HCV infection and liver cirrhosis. Among the base classifiers investigated, the Bayesian Network achieved the highest internal MCC, F1‐score, and accuracy of 0.78, 0.78, and 0.98, respectively (Table 1 and Table 2).…”