Assisting the Diagnosis of Cirrhosis in Chronic Hepatitis C Patients Based on Machine Learning Algorithms: A Novel Non‐Invasive Approach
Makaleler > Tam Makale · 2025
Yazarlar: Emre Dirican, Tayibe Bal, Yusuf Onlen, Figen Sarigul, Ulku User, Nagehan Didem Sari, Behice Kurtaran, Ebubekir Senates, Alper Gunduz, Esra Zerdali, Hasan Karsen, Ayse Batirel, Ridvan Karaali, Hatice Rahmet Guner, Tansu YAMAZHAN, Sukran Kose, Nurettin Erben, Nevin Koc Ince, Iftihar Koksal, Nefise Oztoprak, Gulsen Yoruk, Suheyla Komur, Sibel Yildiz Kaya, Ilkay Bozkurt, Ozgur Gunal, Ilknur Esen Yildiz, Dilara Inan, Sener Barut, Mustafa Namiduru, Selma Tosun, Kamuran Turker, Alper Sener, Kenan Hizel, Nurcan Baykam, Fazilet Duygu, Hurrem Bodur, Guray Can, Hanefi Cem Gul, Ayse Sagmak Tartar, Guven Celebi, Mahmut Sunnetcioglu, Oguz Karabay, Hayat Kumbasar Karaosmanoglu, Fatma Sirmatel, Omer Fehmi Tabak
Journal of Clinical Laboratory Analysis, Uluslararası, Wos, 2025, cilt 39, ss e70054-e70054
DOI: 10.1002/jcla.70054
Özet
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, αlpha‐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