Identifying predictors of varices grading in patients with cirrhosis using ensemble learning

Objectives: 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. Methods: 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. Results: 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). Conclusions: 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.

Angaben zum Objekt

• Standort: Deutsche Nationalbibliothek Frankfurt am Main
• Umfang: Online-Ressource
• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Identifying predictors of varices grading in patients with cirrhosis using ensemble learning ; volume:60 ; number:12 ; year:2022 ; pages:1938-1945 ; extent:08 ; Clinical chemistry and laboratory medicine ; 60, Heft 12 (2022), 1938-1945 (gesamt 08)

Beteiligte, Orts- und Zeitangaben

• Urheber: Bayani, Azadeh ; Hosseini, Azamossadat ; Asadi, Farkhondeh ; Hatami, Behzad ; Kavousi, Kaveh ; Aria, Mehrdad ; Zali, Mohammad Reza

Weitere Informationen

• DOI: 10.1515/cclm-2022-0508
• URN: urn:nbn:de:101:1-2022110313253155732296
• Rechteinformation: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Letzte Aktualisierung: 15.08.2025, 07:31 MESZ