Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
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
_version_ 1784885884396503040
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
work_keys_str_mv AT haraborvaleriu machinelearningapproachesforthepredictionofhepatitisbandcseropositivity
AT mogosraluca machinelearningapproachesforthepredictionofhepatitisbandcseropositivity
AT nechitaaurel machinelearningapproachesforthepredictionofhepatitisbandcseropositivity
AT adamanamaria machinelearningapproachesforthepredictionofhepatitisbandcseropositivity
AT adamgigi machinelearningapproachesforthepredictionofhepatitisbandcseropositivity
AT melintepopescualinasinziana machinelearningapproachesforthepredictionofhepatitisbandcseropositivity
AT melintepopescumarian machinelearningapproachesforthepredictionofhepatitisbandcseropositivity
AT stuparucretumariana machinelearningapproachesforthepredictionofhepatitisbandcseropositivity
AT vasilacheingridandrada machinelearningapproachesforthepredictionofhepatitisbandcseropositivity
AT mihalceanuelena machinelearningapproachesforthepredictionofhepatitisbandcseropositivity
AT carauleanualexandru machinelearningapproachesforthepredictionofhepatitisbandcseropositivity
AT bivoleanuanca machinelearningapproachesforthepredictionofhepatitisbandcseropositivity
AT haraboranamaria machinelearningapproachesforthepredictionofhepatitisbandcseropositivity