A potential new way to facilitate HCV elimination: The prediction of viremia in anti-HCV seropositive patients using machine learning algorithms.
Arab J Gastroenterol
; 25(2): 223-229, 2024 May.
Article
en En
| MEDLINE
| ID: mdl-38705815
ABSTRACT
BACKGROUND AND STUDY AIMS:
The present study was undertaken to design a new machine learning (ML) model that can predict the presence of viremia in hepatitis C virus (HCV) antibody (anti-HCV) seropositive cases. PATIENTS ANDMETHODS:
This retrospective study was conducted between January 2012-January 2022 with 812 patients who were referred for anti-HCV positivity and were examined for HCV ribonucleic acid (HCV RNA). Models were constructed with 11 features with a predictor (presence and absence of viremia) to predict HCV viremia. To build an optimal model, this current study also examined and compared the three classifier data mining approaches RF, SVM and XGBoost.RESULTS:
The highest performance was achieved with XGBoost (90%), which was followed by RF (89%), SVM Linear (85%) and SVM Radial (83%) algorithms, respectively. The four most important key features contributing to the models were alanine aminotransferase (ALT), aspartate aminotransferase (AST), albumin (ALB) and anti-HCV levels, respectively, while "ALB" was replaced by the "AGE" only in the XGBoost model.CONCLUSION:
This study has shown that XGBoost and RF based ML models, incorporating anti-HCV levels and routine laboratory tests (ALT, AST, ALB), and age are capable of providing HCV viremia diagnosis with 90% and 89% accuracy, respectively. These findings highlight the potential of ML models in the early diagnosis of HCV viremia, which may be helpful in optimizing HCV elimination programs.Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Aspartato Aminotransferasas
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Viremia
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ARN Viral
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Hepatitis C
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Anticuerpos contra la Hepatitis C
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Alanina Transaminasa
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Aprendizaje Automático
Límite:
Adult
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
Arab J Gastroenterol
Año:
2024
Tipo del documento:
Article
Pais de publicación:
Egipto