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

• Published in: International journal of environmental research and public health Vol. 20; no. 3; p. 2380
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 29.01.2023; MDPI
• Subjects: Adult; Age; Algorithms; Artificial intelligence; Bayes Theorem; Classification; Decision trees; Ethnicity; Hemodialysis; Hepatitis A; Hepatitis B; Hepatitis B - diagnosis; Hepatitis B - epidemiology; Hepatitis C; Hepatitis C - diagnosis; Hepatitis C - epidemiology; Humans; Infections; Machine Learning; Neural networks; Prospective Studies; Questionnaires; Support Vector Machine; Support vector machines; Viruses; Romania; prediction; screening; machine learning; hepatitis B; hepatitis C
• ISSN: 1660-4601; 1661-7827; 1660-4601
• DOI: 10.3390/ijerph20032380

(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.