Assisting the Diagnosis of Cirrhosis in Chronic Hepatitis C Patients Based on Machine Learning Algorithms: A Novel Non-Invasive Approach
Emre Dirican, Tayibe Bal, Yusuf Onlen, Figen Sarigul, Ulku User, Nefise Oztoprak, Dilara Inan, Nagehan Didem Sari, Gulsen Yoruk, B. Kurtaran, Suheyta Komur, Ebubekir Senates, Alper Gunduz, Esra Zerdali, Hasan Karsen, A. Batirel, Ridvan Karaali, Omer Fehmi Tabak, R. Guner, T. Yamazhan, Sukran Kose, Sener Barut, Nurettin Erben, Nevin Koc Ince, Iftihar Koksal, Sibel Kaya, I. Bozkurt, Ozgur Gunal, Ilknur Esen Yildiz, M. Namiduru, Selma Tosun, K. Turker, Alper Sener, Kenan Hizel, Nurcan Baykam, F. Duygu, Hurrem Bodur, Guray Can, Hanefi Cem Gul, Ayse Sagmak Tartar, G Çelebi, Mahmut Sunnetcioglu, Oguz Karabay, Hayat Kumbasar Karaosmanoglu, Fatma Sirmatel
article (Uluslararası, Hakemli) | Yazar(lar): Emre Dirican, Tayibe Bal, Yusuf Onlen, Figen Sarigul, Ulku User, Nefise Oztoprak, Dilara Inan, Nagehan Didem Sari, Gulsen Yoruk, B. Kurtaran, Suheyta Komur, Ebubekir Senates, Alper Gunduz, Esra Zerdali, Hasan Karsen, A. Batirel, Ridvan Karaali, Omer Fehmi Tabak, R. Guner, T
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