ArticleFrontiers in public health2022
Artificial Intelligence-Based Ensemble Learning Model for Prediction of Hepatitis C Disease.
Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed, 68 citations in OpenAlex.
- Comment on Knowledge assessment of hepatitis C patients regarding their disease at King Abdulaziz University Hospital, Jeddah, Saudi Arabia.Journal of family medicine and primary care · 2026Article
- Machine Learning-Based Prediction of Decompensation in Hepatitis B Virus-Related Cirrhosis.Diagnostics (Basel, Switzerland) · 2025Article
- A study on the effectiveness of machine learning models for hepatitis prediction.Scientific reports · 2025Article
- Machine Learning and Artificial Intelligence for Infectious Disease Surveillance, Diagnosis, and Prognosis.Viruses · 2025Review
- Benchmarking the Confidence of Large Language Models in Answering Clinical Questions: Cross-Sectional Evaluation Study.JMIR medical informatics · 2025Article
- Double-Weighted Bayesian Model Combination for Metabolomics Data Description and Prediction.Metabolites · 2025Article
- Natural products in treating diabetic kidney disease: a visualized bibliometric analysis.Frontiers in pharmacology · 2025Article
- Ensemble for evaluating diagnostic efficacy of non-invasive indices in predicting liver fibrosis in untreated hepatitis C virus population.World journal of methodology · 2024Article
- A comprehensive health assessment approach using ensemble deep learning model for remote patient monitoring with IoT.Scientific reports · 2024Article
- On leveraging self-supervised learning for accurate HCV genotyping.Scientific reports · 2024Article
- Improved liver disease prediction from clinical data through an evaluation of ensemble learning approaches.BMC medical informatics and decision making · 2024Article
- Machine learning for predicting hepatitis B or C virus infection in diabetic patients.Scientific reports · 2023Article
- IHCP: interpretable hepatitis C prediction system based on black-box machine learning models.BMC bioinformatics · 2023Article
- The development of a machine learning algorithm for early detection of viral hepatitis B infection in Nigerian patients.Scientific reports · 2023Article
- A risk prediction model for type 2 diabetes mellitus complicated with retinopathy based on machine learning and its application in health management.Frontiers in medicine · 2023Article
- Hybrid XGBoost model with hyperparameter tuning for prediction of liver disease with better accuracy.World journal of gastroenterology · 2022Article
- Bootstrapping random forest and CHAID for prediction of white spot disease among shrimp farmers.Scientific reports · 2022Article
- Artificial Intelligence-Driven Ensemble Model for Predicting Mortality Due to COVID-19 in East Africa.Diagnostics (Basel, Switzerland) · 2022Article
- Artificial Intelligence-Based Teleopthalmology Application for Diagnosis of Diabetics Retinopathy.IEEE open journal of engineering in medicine and biology · 2022Article
- Weighted Bayesian Belief Network: A Computational Intelligence Approach for Predictive Modeling in Clinical Datasets.Computational intelligence and neuroscience · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
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.