Machine Learning Approaches for the Prediction of Hepatitis B and C Seropositivity

Author:

Harabor Valeriu1,Mogos Raluca2,Nechita Aurel1,Adam Ana-Maria1,Adam Gigi3,Melinte-Popescu Alina-Sinziana4,Melinte-Popescu Marian5,Stuparu-Cretu Mariana6ORCID,Vasilache Ingrid-Andrada2ORCID,Mihalceanu Elena2,Carauleanu Alexandru2ORCID,Bivoleanu Anca2,Harabor Anamaria1

Affiliation:

1. Clinical and Surgical Department, Faculty of Medicine and Pharmacy, ‘Dunarea de Jos’ University, 800216 Galati, Romania

2. Department of Mother and Child, ‘Grigore T. Popa’ University of Medicine and Pharmacy, 700115 Iasi, Romania

3. Department of Pharmaceutical Sciences, Faculty of Medicine and Pharmacy, ‘Dunarea de Jos’ University, 800216 Galati, Romania

4. Department of Mother and Newborn Care, Faculty of Medicine and Biological Sciences, ‘Ștefan cel Mare’ University, 720229 Suceava, Romania

5. Department of Internal Medicine, Faculty of Medicine and Biological Sciences, ‘Ștefan cel Mare’ University, 720229 Suceava, Romania

6. Medical Department, Faculty of Medicine and Pharmacy, ‘Dunarea de Jos’ University, 800216 Galati, Romania

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.

Funder

European Social Fund

Publisher

MDPI AG

Subject

Health, Toxicology and Mutagenesis,Public Health, Environmental and Occupational Health

Reference46 articles.

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2. Kruszon-Moran, D., Paulose-Ram, R., Martin, C.B., Barker, L.K., and McQuillan, G. (2020). Prevalence and Trends in Hepatitis B Virus Infection in the United States, 2015–2018, CDC. NCHS data brief.

3. Update on global epidemiology of viral hepatitis and preventive strategies;Jefferies;World J. Clin. Cases,2018

4. Acute Hepatitis B Virus Infection: Relation of Age to the Clinical Expression of Disease and Subsequent Development of the Carrier State;McMahon;J. Infect. Dis.,1985

5. GBD 2019 Hepatitis B Collaborators (2022). Global, regional, and national burden of hepatitis B, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019. Lancet Gastroenterol. Hepatol., 7, 796–829.

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