ArticleHealthcare informatics research2019
Prediction and Staging of Hepatic Fibrosis in Children with Hepatitis C Virus: A Machine Learning Approach.
Article in Healthcare informatics research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- KASL clinical practice guidelines for noninvasive tests to assess liver fibrosis in chronic liver disease.Clinical and molecular hepatology · 2024Guideline
- Are there any non-invasive methods to predict liver fibrosis in children infected by hepatitis C virus rather than liver biopsy?Clinical and experimental hepatology · 2026Article
- Lipid abnormalities in chronic viral hepatitis: associations and machine learning-enhanced prediction.BMC gastroenterology · 2026Article
- Article
- Transient elastography and diffusion-weighted magnetic resonance imaging for assessment of liver fibrosis in children with chronic hepatitis C.World journal of virology · 2024Article
- Investigation of the effectiveness of a classification method based on improved DAE feature extraction for hepatitis C prediction.Scientific reports · 2024Article
- Thrombospondin-2 as a potential noninvasive biomarker of hepatocyte injury but not liver fibrosis in children with MAFLD: A preliminary study.Clinical and experimental hepatology · 2023Article
- Hybrid model for precise hepatitis-C classification using improved random forest and SVM method.Scientific reports · 2023Article
- Machine Learning Approaches for the Prediction of Hepatitis B and C Seropositivity.International journal of environmental research and public health · 2023Article
- Machine learning insights concerning inflammatory and liver-related risk comorbidities in non-communicable and viral diseases.World journal of gastroenterology · 2022Review
- Diagnosing the Stage of Hepatitis C Using Machine Learning.Journal of healthcare engineering · 2021Article
Corrections and comments
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
objectivesThe aim of this study is to develop an intelligent diagnostic system utilizing machine learning for data cleansing, then build an intelligent model and obtain new cutoff values for APRI (aspartate aminotransferase-to-platelet ratio) and FIB-4 (fibrosis score) for the prediction and staging of fibrosis in children with chronic hepatitis C (CHC).
methodsRandom forest (RF) was utilized in this study for data cleansing; then, prediction and staging of fibrosis, APRI and FIB-4 scores and their areas under the ROC curve (AUC) have been obtained on the cleaned dataset. A cohort of 166 Egyptian children with CHC was studied.
resultsRF, APRI, and FIB-4 achieved high AUCs; where APRI had AUCs of 0.78, 0.816, and 0.77; FIB-4 had AUCs of 0.74, 0.828, and 0.78; and RF had AUCs of 0.903, 0.894, and 0.822, for the prediction of any type of fibrosis, advanced fibrosis, and differentiating between mild and advanced fibrosis, respectively.
conclusionsMachine learning is a valuable addition to non-invasive methods of liver fibrosis prediction and staging in pediatrics. Furthermore, the obtained cutoff values for APRI and FIB-4 showed good performance and are consistent with some previously obtained cutoff values. There was some agreement between the predictions of RF, APRI and FIB-4 for the prediction and staging of fibrosis.
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