Author
Listed:
- Valeriu Harabor
(Clinical and Surgical Department, Faculty of Medicine and Pharmacy, ‘Dunarea de Jos’ University, 800216 Galati, Romania)
- Raluca Mogos
(Department of Mother and Child, ‘Grigore T. Popa’ University of Medicine and Pharmacy, 700115 Iasi, Romania)
- Aurel Nechita
(Clinical and Surgical Department, Faculty of Medicine and Pharmacy, ‘Dunarea de Jos’ University, 800216 Galati, Romania)
- Ana-Maria Adam
(Clinical and Surgical Department, Faculty of Medicine and Pharmacy, ‘Dunarea de Jos’ University, 800216 Galati, Romania)
- Gigi Adam
(Department of Pharmaceutical Sciences, Faculty of Medicine and Pharmacy, ‘Dunarea de Jos’ University, 800216 Galati, Romania)
- Alina-Sinziana Melinte-Popescu
(Department of Mother and Newborn Care, Faculty of Medicine and Biological Sciences, ‘Ștefan cel Mare’ University, 720229 Suceava, Romania)
- Marian Melinte-Popescu
(Department of Internal Medicine, Faculty of Medicine and Biological Sciences, ‘Ștefan cel Mare’ University, 720229 Suceava, Romania)
- Mariana Stuparu-Cretu
(Medical Department, Faculty of Medicine and Pharmacy, ‘Dunarea de Jos’ University, 800216 Galati, Romania)
- Ingrid-Andrada Vasilache
(Department of Mother and Child, ‘Grigore T. Popa’ University of Medicine and Pharmacy, 700115 Iasi, Romania)
- Elena Mihalceanu
(Department of Mother and Child, ‘Grigore T. Popa’ University of Medicine and Pharmacy, 700115 Iasi, Romania)
- Alexandru Carauleanu
(Department of Mother and Child, ‘Grigore T. Popa’ University of Medicine and Pharmacy, 700115 Iasi, Romania)
- Anca Bivoleanu
(Department of Mother and Child, ‘Grigore T. Popa’ University of Medicine and Pharmacy, 700115 Iasi, Romania)
- Anamaria Harabor
(Clinical and Surgical 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.
Suggested Citation
Valeriu Harabor & Raluca Mogos & Aurel Nechita & Ana-Maria Adam & Gigi Adam & Alina-Sinziana Melinte-Popescu & Marian Melinte-Popescu & Mariana Stuparu-Cretu & Ingrid-Andrada Vasilache & Elena Mihalce, 2023.
"Machine Learning Approaches for the Prediction of Hepatitis B and C Seropositivity,"
IJERPH, MDPI, vol. 20(3), pages 1-9, January.
Handle:
RePEc:gam:jijerp:v:20:y:2023:i:3:p:2380-:d:1050143
Download full text from publisher
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:20:y:2023:i:3:p:2380-:d:1050143. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.