ArticleInternational journal of environmental research and public health2023
Machine Learning Approaches for the Prediction of Hepatitis B and C Seropositivity.
Article in International journal of environmental research and public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it, 34 citations in OpenAlex.
- Risk prediction models for targeted testing of HIV, hepatitis B and hepatitis C: a systematic review and meta-analysis.BMC infectious diseases · 2025Pooled it
- Diagnostic Prediction Models for Primary Care, Based on AI and Electronic Health Records: Systematic Review.JMIR medical informatics · 2025Pooled it
- Development of an Explainable Machine Learning Model to Predict Mortality Risk in Sepsis Patients: Insights From a Real-World Clinical Data.Shock (Augusta, Ga.) · 2026Article
- A study on the effectiveness of machine learning models for hepatitis prediction.Scientific reports · 2025Article
- A cross dataset meta-model for hepatitis C detection using multi-dimensional pre-clustering.Scientific reports · 2025Article
- Predicting Adverse Neurodevelopmental Outcomes in Premature Neonates with Intrauterine Growth Restriction Using a Three-Layered Neural Network.Diagnostics (Basel, Switzerland) · 2025Article
- Machine Learning Approach and Bioinformatics Analysis Discovered Key Genomic Signatures for Hepatitis B Virus-Associated Hepatocyte Remodeling and Hepatocellular Carcinoma.Cancer informatics · 2025Review
- Review
- Machine Learning-Based Algorithms for Enhanced Prediction of Local Recurrence and Metastasis in Low Rectal Adenocarcinoma Using Imaging, Surgical, and Pathological Data.Diagnostics (Basel, Switzerland) · 2024Article
- Predicting the Feasibility of Curative Resection in Low Rectal Cancer: Insights from a Prospective Observational Study on Preoperative Magnetic Resonance Imaging Accuracy.Medicina (Kaunas, Lithuania) · 2024Observational
- Pregnancy Outcomes in Patients with Urosepsis and Uncomplicated Urinary Tract Infections-A Retrospective Study.Medicina (Kaunas, Lithuania) · 2023Article
Corrections and comments
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Authors and funding
13 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
(1) Background: The identification of patients at risk for hepatitis B and C viral infection is a challenge for the clinicians and public health specialists. The aim of this study was to evaluate and compare the predictive performances of four machine learning-based models for the prediction of HBV and HCV status. (2) Methods: This prospective cohort screening study evaluated adults from the North-Eastern and South-Eastern regions of Romania between January 2022 and November 2022 who underwent viral hepatitis screening in their family physician's offices. The patients' clinical characteristics were extracted from a structured survey and were included in four machine learning-based models: support vector machine (SVM), random forest (RF), naïve Bayes (NB), and K nearest neighbors (KNN), and their predictive performance was assessed. (3) Results: All evaluated models performed better when used to predict HCV status. The highest predictive performance was achieved by KNN algorithm (accuracy: 98.1%), followed by SVM and RF with equal accuracies (97.6%) and NB (95.7%). The predictive performance of these models was modest for HBV status, with accuracies ranging from 78.2% to 97.6%. (4) Conclusions: The machine learning-based models could be useful tools for HCV infection prediction and for the risk stratification process of adult patients who undergo a viral hepatitis screening program.
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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 Socratic graph.