{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T16:27:01Z","timestamp":1782404821380,"version":"3.54.5"},"reference-count":30,"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-2022-0454_abs_001\">\n                  <jats:title>Objectives<\/jats:title>\n                  <jats:p>The aim of the study was to implement a non-invasive model to predict ascites grades among patients with cirrhosis.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec id=\"j_cclm-2022-0454_abs_002\">\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>In the present study, we used modern machine learning (ML) methods to develop a scoring system solely based on routine laboratory and clinical data to help physicians accurately diagnose and predict different degrees of ascites. We used ANACONDA3\u20135.2.0 64\u00a0bit, free and open-source platform distribution of Python programming language with numerous modules, packages, and rich libraries that provide various methods for classification problems. Through the 10-fold cross-validation, we employed three common learning models on our dataset, k-nearest neighbors (KNN), support vector machine (SVM), and neural network classification algorithms.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec id=\"j_cclm-2022-0454_abs_003\">\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>According to the data received from the research institute, three types of data analysis have been performed. The algorithms used to predict ascites were KNN, cross-validation (CV), and multilayer perceptron neural networks (MLPNN), which achieved an average accuracy of 94, 91, and 90%, respectively. Also, in the average accuracy of the algorithms, KNN had the highest accuracy of 94%.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec id=\"j_cclm-2022-0454_abs_004\">\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>We applied well-known ML approaches to predict ascites. The findings showed a strong performance compared to the classical statistical approaches. This ML-based approach can help to avoid unnecessary risks and costs for patients with acute stages of the disease.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1515\/cclm-2022-0454","type":"journal-article","created":{"date-parts":[[2022,5,23]],"date-time":"2022-05-23T20:22:28Z","timestamp":1653337348000},"page":"1946-1954","source":"Crossref","is-referenced-by-count":22,"title":["Machine learning-based system for prediction of ascites grades in patients with liver cirrhosis using laboratory and clinical data: design and implementation study"],"prefix":"10.1515","volume":"60","author":[{"given":"Behzad","family":"Hatami","sequence":"first","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":"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":"Azadeh","family":"Bayani","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":"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"}]},{"given":"Kaveh","family":"Kavousi","sequence":"additional","affiliation":[{"name":"Laboratory of Complex Biological Systems and Bioinformatics (CBB), Institute of Biochemistry and Biophysics (IBB), University of Tehran , Tehran , Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","published-online":{"date-parts":[[2022,5,24]]},"reference":[{"key":"2023033111540091389_j_cclm-2022-0454_ref_001","doi-asserted-by":"crossref","unstructured":"Fedeli, U, Avossa, F, Guzzinati, S, Bovo, E, Saugo, M. 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