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

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
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    References listed on IDEAS

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    1. Hiroaki Haga & Hidenori Sato & Ayumi Koseki & Takafumi Saito & Kazuo Okumoto & Kyoko Hoshikawa & Tomohiro Katsumi & Kei Mizuno & Taketo Nishina & Yoshiyuki Ueno, 2020. "A machine learning-based treatment prediction model using whole genome variants of hepatitis C virus," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-12, November.
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