Machine learning-based system for prediction of ascites grades in patients with liver cirrhosis using laboratory and clinical data: design and implementation study.

Journal: Clinical chemistry and laboratory medicine

PMID: 35607284

OBJECTIVES: The aim of the study was to implement a non-invasive model to predict ascites grades among patients with cirrhosis.

Behzad Hatami

Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Farkhondeh Asadi

Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Azadeh Bayani

Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Mohammad Reza Zali

Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Kaveh Kavousi

Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran.

Algorithms Ascites Humans Liver Cirrhosis Machine Learning Neural Networks, Computer Support Vector Machine

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