{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,4]],"date-time":"2026-11-04T17:15:03Z","timestamp":1793812503512,"version":"4.6.1"},"reference-count":32,"publisher":"Walter de Gruyter GmbH","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,11,25]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec id=\"j_cclm-2022-0623_abs_001\">\n                  <jats:title>Objectives<\/jats:title>\n                  <jats:p>All patients with cirrhosis should be periodically examined for esophageal varices (EV), however, a large percentage of patients undergoing screening, do not\u00a0have EV or have only mild EV and do not have high-risk\u00a0characteristics. Therefore, developing a non-invasive method to predict the occurrence of EV in patients with liver cirrhosis as a non-invasive method with high accuracy seems useful. In the present research, we compared the performance of several machine learning (ML) methods to predict EV on laboratory and clinical data to choose the best model.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec id=\"j_cclm-2022-0623_abs_002\">\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>Four-hundred-and-ninety data from the Liver and Gastroenterology Research Center of Shahid Beheshti University of Medical Sciences in the period 2014\u20132021, were analyzed applying models including random forest (RF), artificial neural network (ANN), support vector machine (SVM), and logistic regression.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec id=\"j_cclm-2022-0623_abs_003\">\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>RF and SVM had the best results in general for all grades of EV. RF showed remarkably better results and the highest area under the curve (AUC). After that, SVM and ANN had the AUC of 98%, for grade 3, the SVM algorithm had the highest AUC after RF (89%).<\/jats:p>\n               <\/jats:sec>\n               <jats:sec id=\"j_cclm-2022-0623_abs_004\">\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>The findings may help to better predict EV with high precision and accuracy and also can help reduce the burden of frequent visits to endoscopic centers. It can also help practitioners to manage cirrhosis by predicting EV with lower costs.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1515\/cclm-2022-0623","type":"journal-article","created":{"date-parts":[[2022,8,31]],"date-time":"2022-08-31T20:47:37Z","timestamp":1661978857000},"page":"1955-1962","source":"Crossref","is-referenced-by-count":21,"title":["Performance of machine learning techniques on prediction of esophageal varices grades among patients with cirrhosis"],"prefix":"10.1515","volume":"60","author":[{"given":"Azadeh","family":"Bayani","sequence":"first","affiliation":[{"name":"Department of Health Information Technology and Management , School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences , Tehran , Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Farkhondeh","family":"Asadi","sequence":"additional","affiliation":[{"name":"Department of Health Information Technology and Management , School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences , Tehran , Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Azamossadat","family":"Hosseini","sequence":"additional","affiliation":[{"name":"Department of Health Information Technology and Management , School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences , Tehran , Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Behzad","family":"Hatami","sequence":"additional","affiliation":[{"name":"Gastroenterology and Liver Diseases Research Center , Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences , Tehran , Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaveh","family":"Kavousi","sequence":"additional","affiliation":[{"name":"Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics , Institute of Biochemistry and Biophysics (IBB), University of Tehran , Tehran , Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6027-6952","authenticated-orcid":false,"given":"Mehrad","family":"Aria","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology and Computer Engineering, Azarbaijan Shahid Madani University , Tabriz , Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad Reza","family":"Zali","sequence":"additional","affiliation":[{"name":"Gastroenterology and Liver Diseases Research Center , Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences , Tehran , Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","published-online":{"date-parts":[[2022,9,2]]},"reference":[{"key":"2023033111540079344_j_cclm-2022-0623_ref_001","doi-asserted-by":"crossref","unstructured":"Abd El-Salam, SM, Ezz, MM, Hashem, S, Elakel, W, Salama, R, ElMakhzangy, H, et al.. 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