Identifying predictors of varices grading in patients with cirrhosis using ensemble learning.
cirrhosis
ensemble learning
esophageal varices
liver disease
machine learning
prediction
Journal
Clinical chemistry and laboratory medicine
ISSN: 1437-4331
Titre abrégé: Clin Chem Lab Med
Pays: Germany
ID NLM: 9806306
Informations de publication
Date de publication:
25 11 2022
25 11 2022
Historique:
received:
24
05
2022
accepted:
28
06
2022
pubmed:
20
7
2022
medline:
5
11
2022
entrez:
19
7
2022
Statut:
epublish
Résumé
The present study was conducted to improve the performance of predictive methods by introducing the most important factors which have the highest effects on the prediction of esophageal varices (EV) grades among patients with cirrhosis. In the present study, the ensemble learning methods, including Catboost and XGB classifier, were used to choose the most potent predictors of EV grades solely based on routine laboratory and clinical data, a dataset of 490 patients with cirrhosis gathered. To increase the validity of the results, a five-fold cross-validation method was applied. The model was conducted using python language, Anaconda open-source platform. TRIPOD checklist for prediction model development was completed. The Catboost model predicted all the targets correctly with 100% precision. However, the XGB classifier had the best performance for predicting grades 0 and 1, and totally the accuracy was 91.02%. The most significant variables, according to the best performing model, which was CatBoost, were child score, white blood cell (WBC), vitalism K (K), and international normalized ratio (INR). Using machine learning models, especially ensemble learning models, can remarkably increase the prediction performance. The models allow practitioners to predict EV risk at any clinical visit and decrease unneeded esophagogastroduodenoscopy (EGD) and consequently reduce morbidity, mortality, and cost of the long-term follow-ups for patients with cirrhosis.
Identifiants
pubmed: 35852068
pii: cclm-2022-0508
doi: 10.1515/cclm-2022-0508
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1938-1945Informations de copyright
© 2022 Walter de Gruyter GmbH, Berlin/Boston.
Références
Aleksić, A, Nedeljković, S, Jovanović, M, Ranđelović, M, Vuković, M, Stojanović, V, et al.. Prediction of important factors for bleeding in liver cirrhosis disease using ensemble data mining approach. Mathematics 2020;8:1887.
Yeom, SK, Lee, CH, Cha, SH, Park, CM. Prediction of liver cirrhosis, using diagnostic imaging tools. World J Hepatol 2015;7:2069. https://doi.org/10.4254/wjh.v7.i17.2069.
Yan, Y, Li, Y, Fan, C, Zhang, Y, Zhang, S, Wang, Z, et al.. A novel machine learning-based radiomic model for diagnosing high bleeding risk esophageal varices in cirrhotic patients. Hepatol Int 2021;16:423–32.
Dong, TS, Kalani, A, Aby, ES, Le, L, Luu, K, Hauer, M, et al.. Machine learning-based development and validation of a scoring system for screening high-risk esophageal varices. Clin Gastroenterol Hepatol 2019;17:1894–901. e1891. https://doi.org/10.1016/j.cgh.2019.01.025.
Garcia‐Tsao, G, Abraldes, JG, Berzigotti, A, Bosch, J. Portal hypertensive bleeding in cirrhosis: risk stratification, diagnosis, and management: 2016 practice guidance by the American Association for the study of liver diseases. Hepatology 2017;65:310–35. https://doi.org/10.1002/hep.28906.
Baltes, A, Akhtar, W, Birstler, J, Olson-Streed, H, Eagen, K, Seal, D, et al.. Predictors of skin and soft tissue infections among sample of rural residents who inject drugs. Harm Reduct J 2020;17:96. https://doi.org/10.1186/s12954-020-00447-3.
Pateu, E, Oberti, F, Calès, P. The noninvasive diagnosis of esophageal varices and its application in clinical practice. Clin Res Hepatol Gastroenterol 2018;42:6–16. https://doi.org/10.1016/j.clinre.2017.07.006.
Haq, I, Tripathi, D. Recent advances in the management of variceal bleeding. Gastroenterol Rep 2017;5:113–26. https://doi.org/10.1093/gastro/gox007.
Chicco, D, Jurman, G. An ensemble learning approach for enhanced classification of patients with hepatitis and cirrhosis. IEEE Access 2021;9:24485–98. https://doi.org/10.1109/access.2021.3057196.
Colli, A, Gana, JC, Yap, J, Adams‐Webber, T, Rashkovan, N, Ling, SC, et al.. Platelet count, spleen length, and platelet count-to-spleen length ratio for the diagnosis of oesophageal varices in people with chronic liver disease or portal vein thrombosis. Cochrane Database Syst Rev 2017;26:CD008759. https://doi.org/10.1002/14651858.CD008759.pub2.
De Franchis, R. Expanding consensus in portal hypertension: report of the Baveno VI consensus workshop: stratifying risk and individualizing care for portal hypertension. J Hepatol 2015;63:743–52. https://doi.org/10.1016/j.jhep.2015.05.022.
Sousa, M, Sousa Fernandes, S, Proença, L, Silva, AP, Leite, S, Silva, J, et al.. The Baveno VI criteria for predicting esophageal varices: validation in real life practice. Rev Esp Enferm Dig 2017;109:704–7. https://doi.org/10.17235/reed.2017.5052/2017.
Jordan, MI, Mitchell, TM. Machine learning: trends, perspectives, and prospects. Science 2015;349:255–60. https://doi.org/10.1126/science.aaa8415.
Wu, CC, Yeh, WC, Hsu, WD, Islam, MM, Nguyen, PAA, Poly, TN, et al.. Prediction of fatty liver disease using machine learning algorithms. Comput Methods Progr Biomed 2019;170:23–9. https://doi.org/10.1016/j.cmpb.2018.12.032.
Şimşek, C, Tekin, E, Sahin, H, Sahin, TK, Balaban, YH. Artificial intelligence to predict esophageal varices in patients with cirrhosis. Acıbadem Üniversitesi Sağlık Bilimleri Dergisi 2021;12:625–9.
Abd El-Salam, SM, Ezz, MM, Hashem, S, Elakel, W, Salama, R, ElMakhzangy, H, et al.. Performance of machine learning approaches on prediction of esophageal varices for Egyptian chronic hepatitis C patients. Inform Med Unlocked 2019;17:100267. https://doi.org/10.1016/j.imu.2019.100267.
Krige, J, Spence, RT, Jonas, E, Hoogerboord, M, Ellsmere, J. A new recalibrated four-category child–pugh score performs better than the original child–pugh and MELD scores in predicting in-hospital mortality in decompensated alcoholic cirrhotic patients with acute variceal bleeding: a real-world cohort analysis. World J Surg 2020;44:241–6. https://doi.org/10.1007/s00268-019-05211-8.
Ghosh, P, Azam, S, Karim, A, Hassan, M, Roy, K, Jonkman, M. A comparative study of different machine learning tools in detecting diabetes. Procedia Comput Sci 2021;192:467–77. https://doi.org/10.1016/j.procs.2021.08.048.
Latha, CBC, Jeeva, SC. Improving the accuracy of prediction of heart disease risk based on ensemble classification techniques. Inform Med Unlocked 2019;16:100203. https://doi.org/10.1016/j.imu.2019.100203.
Hancock, JT, Khoshgoftaar, TM. CatBoost for big data: an interdisciplinary review. J Big Data 2020;7:94. https://doi.org/10.1186/s40537-020-00369-8.
Luo, M, Wang, Y, Xie, Y, Zhou, L, Qiao, J, Qiu, S, et al.. Combination of feature selection and CatBoost for prediction: the first application to the estimation of aboveground biomass. Forests 2021;12:216. https://doi.org/10.3390/f12020216.
Jabeur, SB, Gharib, C, Mefteh-Wali, S, Arfi, WB. CatBoost model and artificial intelligence techniques for corporate failure prediction. Technol Forecast Soc Change 2021;166:120658. https://doi.org/10.1016/j.techfore.2021.120658.
Jo, YY, Han, J, Park, HW, Jung, H, Lee, JD, Jung, J, et al.. Prediction of prolonged length of hospital stay after cancer surgery using machine learning on electronic health records: retrospective cross-sectional study. JMIR Med Inform 2021;9:e23147. https://doi.org/10.2196/23147.
Kropf, M, Hayn, D, Morris, D, Radhakrishnan, AK, Belyavskiy, E, Frydas, A, et al.. Cardiac anomaly detection based on time and frequency domain features using tree-based classifiers. Physiol Meas 2018;39:114001. https://doi.org/10.1088/1361-6579/aae13e.
Queipo, NV, Nava, E. A gradient boosting approach with diversity promoting measures for the ensemble of surrogates in engineering. Struct Multidiscip Optim 2019;60:1289–311. https://doi.org/10.1007/s00158-019-02325-4.
Agarwal, S, Sharma, S, Kumar, M, Venishetty, S, Bhardwaj, A, Kaushal, K, et al.. Development of a machine learning model to predict bleed in esophageal varices in compensated advanced chronic liver disease: a proof of concept. J Gastroenterol Hepatol 2021;36:2935–42. https://doi.org/10.1111/jgh.15560.
Shung, DL, Au, B, Taylor, RA, Tay, JK, Laursen, SB, Stanley, AJ, et al.. Validation of a machine learning model that outperforms clinical risk scoring systems for upper gastrointestinal bleeding. Gastroenterology 2020;158:160–7. https://doi.org/10.1053/j.gastro.2019.09.009.
Zoli, M, Merkel, C, Magalotti, D, Gueli, C, Grimaldi, M, Gatta, A, et al.. Natural history of cirrhotic patients with small esophageal varices: a prospective study. Am J Gastroenterol 2000;95:503–8. https://doi.org/10.1111/j.1572-0241.2000.01775.x.
Hong, Wd, Zhu, Qh, Huang, Zm, Chen, Xr, Jiang, Zc, Xu, Sh, et al.. Predictors of esophageal varices in patients with HBV-related cirrhosis: a retrospective study. BMC Gastroenterol 2009;9:1–7. https://doi.org/10.1186/1471-230X-9-11.
Trebicka, J, Gu, W, Ibáñez-Samaniego, L, Hernández-Gea, V, Pitarch, C, Garcia, E, et al.. Rebleeding and mortality risk are increased by ACLF but reduced by pre-emptive tips. J Hepatol 2020;73:1082–91. https://doi.org/10.1016/j.jhep.2020.04.024.
Amitrano, L, Guardascione, MA, Bennato, R, Manguso, F, Balzano, A. MELD score and hepatocellular carcinoma identify patients at different risk of short-term mortality among cirrhotics bleeding from esophageal varices. J Hepatol 2005;42:820–5. https://doi.org/10.1016/j.jhep.2005.01.021.
Abd-Elsalam, SM, Ezz, MM, Gamalel-Din, S, Esmat, G, Salama, A, ElHefnawi, M. Early diagnosis of esophageal varices using Boosted-Naïve Bayes Tree: a multicenter cross-sectional study on chronic hepatitis C patients. Inform Med Unlocked 2020;20:100421. https://doi.org/10.1016/j.imu.2020.100421.
Chu, A, Ahn, H, Halwan, B, Kalmin, B, Artifon, EL, Barkun, A, et al.. A decision support system to facilitate management of patients with acute gastrointestinal bleeding. Artif Intell Med 2008;42:247–59. https://doi.org/10.1016/j.artmed.2007.10.003.
Das, A, Ben-Menachem, T, Cooper, GS, Chak, A, Sivak, MVJr, Gonet, JA, et al.. Prediction of outcome in acute lower-gastrointestinal haemorrhage based on an artificial neural network: internal and external validation of a predictive model. Lancet 2003;362:1261–6. https://doi.org/10.1016/s0140-6736(03)14568-0.
Choi, C, Swingland, J, Ali, A, Bose, S, Ayaru, L. PMO-204 Assessing risk of adverse outcome in acute lower gastrointestinal bleeding: artificial neural network vs sign guidelines and bleed score. Gut 2012;61:A156–7. https://doi.org/10.1136/gutjnl-2012-302514b.204.
Augustin, S, Muntaner, L, Altamirano, JT, González, A, Saperas, E, Dot, J, et al.. Predicting early mortality after acute variceal hemorrhage based on classification and regression tree analysis. Clin Gastroenterol Hepatol 2009;7:1347–54. https://doi.org/10.1016/j.cgh.2009.08.011.
Loftus, TJ, Brakenridge, SC, Croft, CA, Smith, RS, Efron, PA, Moore, FA, et al.. Neural network prediction of severe lower intestinal bleeding and the need for surgical intervention. J Surg Res 2017;212:42–7. https://doi.org/10.1016/j.jss.2016.12.032.