Assisting the Diagnosis of Cirrhosis in Chronic Hepatitis C Patients Based on Machine Learning Algorithms: A Novel Non-Invasive Approach
dc.authorid | Yildiz Kaya, Sibel/0000-0002-6319-7889; | |
dc.contributor.author | Dirican, Emre | |
dc.contributor.author | Bal, Tayibe | |
dc.contributor.author | Onlen, Yusuf | |
dc.contributor.author | Sarigul, Figen | |
dc.contributor.author | User, Ulku | |
dc.contributor.author | Sari, Nagehan Didem | |
dc.contributor.author | Kurtaran, Behice | |
dc.date.accessioned | 2025-10-11T20:48:49Z | |
dc.date.available | 2025-10-11T20:48:49Z | |
dc.date.issued | 2025 | |
dc.department | Düzce Üniversitesi | en_US |
dc.description.abstract | Aim: 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 | en_US |
dc.identifier.doi | 10.1002/jcla.70054 | |
dc.identifier.issn | 0887-8013 | |
dc.identifier.issn | 1098-2825 | |
dc.identifier.issue | 12 | en_US |
dc.identifier.pmid | 40384539 | en_US |
dc.identifier.scopus | 2-s2.0-105005551496 | en_US |
dc.identifier.scopusquality | Q1 | en_US |
dc.identifier.uri | https://doi.org/10.1002/jcla.70054 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12684/22121 | |
dc.identifier.volume | 39 | en_US |
dc.identifier.wos | WOS:001490437900001 | en_US |
dc.identifier.wosquality | Q2 | en_US |
dc.indekslendigikaynak | Web of Science | en_US |
dc.indekslendigikaynak | Scopus | en_US |
dc.indekslendigikaynak | PubMed | en_US |
dc.language.iso | en | en_US |
dc.publisher | Wiley | en_US |
dc.relation.ispartof | Journal of Clinical Laboratory Analysis | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.snmz | KA_WOS_20250911 | |
dc.subject | alfa-feto protein | en_US |
dc.subject | chronic hepatitis C | en_US |
dc.subject | classification | en_US |
dc.subject | diagnosis of cirrhosis | en_US |
dc.subject | machine learning | en_US |
dc.title | Assisting the Diagnosis of Cirrhosis in Chronic Hepatitis C Patients Based on Machine Learning Algorithms: A Novel Non-Invasive Approach | en_US |
dc.type | Article | en_US |