ArticleJournal of healthcare engineering2021
Diagnosing the Stage of Hepatitis C Using Machine Learning.
Article in Journal of healthcare engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 39 citations in OpenAlex.
- Cross-Model Explainability Consistency in Hepatitis C Stage Classification: A SHAP, LIME, and Counterfactual Analysis Across Five Machine Learning Architectures.Diagnostics (Basel, Switzerland) · 2026Article
- Oncoviruses in the Oral Cavity: Recent Advances in Understanding Viral Infections and Tumorigenesis.International journal of molecular sciences · 2025Review
- Assisting the Diagnosis of Cirrhosis in Chronic Hepatitis C Patients Based on Machine Learning Algorithms: A Novel Non-Invasive Approach.Journal of clinical laboratory analysis · 2025Observational
- Advancements in the diagnosis of biliopancreatic diseases: A comparative review and study on future insights.World journal of gastrointestinal endoscopy · 2025Review
- A cross dataset meta-model for hepatitis C detection using multi-dimensional pre-clustering.Scientific reports · 2025Article
- Article
- Integrated neural network and evolutionary algorithm approach for liver fibrosis staging: Can artificial intelligence reduce patient costs?JGH open : an open access journal of gastroenterology and hepatology · 2024Article
- Machine Learning Approaches for the Prediction of Hepatitis B and C Seropositivity.International journal of environmental research and public health · 2023Article
- Efficient Data-Driven Machine Learning Models for Cardiovascular Diseases Risk Prediction.Sensors (Basel, Switzerland) · 2023Article
- Machine learning insights concerning inflammatory and liver-related risk comorbidities in non-communicable and viral diseases.World journal of gastroenterology · 2022Review
- Decision Support System for Predicting Survivability of Hepatitis Patients.Frontiers in public health · 2022Article
- Design and Implementation of 3D Animation Data Processing Development Platform Based on Artificial Intelligence.Computational intelligence and neuroscience · 2022Article
Corrections and comments
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Authors and funding
7 authors at 5 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hepatitis C is a prevalent disease in the world. Around 3 to 4 million new cases of Hepatitis C are reported every year across the globe. Effective, timely prediction of the disease can help people know about their Stage of Hepatitis C. To identify the Stage of disease, various noninvasive serum biochemical markers and clinical information of the patients have been used. Machine learning techniques have been an effective alternative tool for determining the Stage of this chronic disease of the liver to prevent biopsy side effects. In this study, an Intelligent Hepatitis C Stage Diagnosis System (IHSDS) empowered with machine learning is presented to predict the Stage of Hepatitis C in a human using Artificial Neural Network (ANN). The dataset obtained from the UCI machine learning repository contains 29 features, out of which the 19 most reverent are selected to conduct the study; 70% of the dataset is used for training and 30% for validation purposes. The precision value is compared with the proposed IHSDS with previously presented models. The proposed IHSDS has achieved 98.89% precision during training and 94.44% precision during validation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.