Assisting the Diagnosis of Cirrhosis in Chronic Hepatitis C Patients Based on Machine Learning Algorithms: A Novel Non-Invasive Approach

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dc.contributor.authorDirican, Emre
dc.contributor.authorBal, Tayibe
dc.contributor.authorÖnlen, Yusuf
dc.contributor.authorSarıgül, Figen
dc.contributor.authorBaykam, Nurcan
dc.date.accessioned2026-03-31T13:21:21Z
dc.date.available2026-03-31T13:21:21Z
dc.date.issued2025
dc.description.abstractAim: This study aimed to determine the important features and cut-off values after demonstrating the detectability of cirrhosis using routine laboratory test results of chronic hepatitis C (CHC) patients in machine learning (ML) algorithms. Methods: This retrospective multicenter (37 referral centers) study included the data obtained from the Hepatitis C Turkey registry of 1164 patients with biopsy-proven CHC. Three different ML algorithms were used to classify the presence/absence of cirrhosis with the determined features. Results: The highest performance in the prediction of cirrhosis (Accuracy = 0.89, AUC = 0.87) was obtained from the Random Forest (RF) method. The five most important features that contributed to the classification were platelet, alpha lpha-feto protein (AFP), age, gamma-glutamyl transferase (GGT), and prothrombin time (PT). The cut-off values of these features were obtained as platelet < 182.000/mm3, AFP > 5.49 ng/mL, age > 52 years, GGT > 39.9 U/L, and PT > 12.35 s. Using cut-off values, the risk coefficients were AOR = 4.82 for platelet, AOR = 3.49 for AFP, AOR = 4.32 for age, AOR = 3.04 for GGT, and AOR = 2.20 for PT. Conclusion: These findings indicated that the RF-based ML algorithm could classify cirrhosis with high accuracy. Thus, crucial features and cut-off values for physicians in the detection of cirrhosis were determined. In addition, although AFP is not included in non-invasive indexes, it had a remarkable contribution in predicting cirrhosis. Trial Registration: Clinicaltrials.gov identifier: NCT03145844
dc.identifier.doi10.1002/jcla.70054
dc.identifier.issn0887-8013
dc.identifier.issn1098-2825
dc.identifier.issue12
dc.identifier.pmid40384539
dc.identifier.urihttp://dx.doi.org/10.1002/jcla.70054
dc.identifier.urihttps://hdl.handle.net/11491/9711
dc.identifier.volume39
dc.identifier.wosWOS:001490437900001
dc.language.isoen
dc.publisherWILEY
dc.relation.ispartofJ CLIN LAB ANAL
dc.subjectalfa-feto protein
dc.subjectchronic hepatitis C
dc.subjectclassification
dc.subjectdiagnosis of cirrhosis
dc.subjectmachine learning
dc.titleAssisting the Diagnosis of Cirrhosis in Chronic Hepatitis C Patients Based on Machine Learning Algorithms: A Novel Non-Invasive Approach
dc.typeArticle

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