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Machine Learning Approaches for the Prediction of Hepatitis B and C Seropositivity
(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...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9915359/ https://www.ncbi.nlm.nih.gov/pubmed/36767747 http://dx.doi.org/10.3390/ijerph20032380 |
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author | Harabor, Valeriu Mogos, Raluca Nechita, Aurel Adam, Ana-Maria Adam, Gigi Melinte-Popescu, Alina-Sinziana Melinte-Popescu, Marian Stuparu-Cretu, Mariana Vasilache, Ingrid-Andrada Mihalceanu, Elena Carauleanu, Alexandru Bivoleanu, Anca Harabor, Anamaria |
author_facet | Harabor, Valeriu Mogos, Raluca Nechita, Aurel Adam, Ana-Maria Adam, Gigi Melinte-Popescu, Alina-Sinziana Melinte-Popescu, Marian Stuparu-Cretu, Mariana Vasilache, Ingrid-Andrada Mihalceanu, Elena Carauleanu, Alexandru Bivoleanu, Anca Harabor, Anamaria |
author_sort | Harabor, Valeriu |
collection | PubMed |
description | (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. |
format | Online Article Text |
id | pubmed-9915359 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-99153592023-02-11 Machine Learning Approaches for the Prediction of Hepatitis B and C Seropositivity Harabor, Valeriu Mogos, Raluca Nechita, Aurel Adam, Ana-Maria Adam, Gigi Melinte-Popescu, Alina-Sinziana Melinte-Popescu, Marian Stuparu-Cretu, Mariana Vasilache, Ingrid-Andrada Mihalceanu, Elena Carauleanu, Alexandru Bivoleanu, Anca Harabor, Anamaria Int J Environ Res Public Health Article (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. MDPI 2023-01-29 /pmc/articles/PMC9915359/ /pubmed/36767747 http://dx.doi.org/10.3390/ijerph20032380 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Harabor, Valeriu Mogos, Raluca Nechita, Aurel Adam, Ana-Maria Adam, Gigi Melinte-Popescu, Alina-Sinziana Melinte-Popescu, Marian Stuparu-Cretu, Mariana Vasilache, Ingrid-Andrada Mihalceanu, Elena Carauleanu, Alexandru Bivoleanu, Anca Harabor, Anamaria Machine Learning Approaches for the Prediction of Hepatitis B and C Seropositivity |
title | Machine Learning Approaches for the Prediction of Hepatitis B and C Seropositivity |
title_full | Machine Learning Approaches for the Prediction of Hepatitis B and C Seropositivity |
title_fullStr | Machine Learning Approaches for the Prediction of Hepatitis B and C Seropositivity |
title_full_unstemmed | Machine Learning Approaches for the Prediction of Hepatitis B and C Seropositivity |
title_short | Machine Learning Approaches for the Prediction of Hepatitis B and C Seropositivity |
title_sort | machine learning approaches for the prediction of hepatitis b and c seropositivity |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9915359/ https://www.ncbi.nlm.nih.gov/pubmed/36767747 http://dx.doi.org/10.3390/ijerph20032380 |
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