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

Objectives: The aim of the study was to implement a non-invasive model to predict ascites grades among patients with cirrhosis. Methods: 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 ANACONDA3–5.2.0 64 bit, 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. Results: 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%. Conclusions: 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.

Angaben zum Objekt

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

Verweise und Beziehungen

• Erschienen in: Machine learning-based system for prediction of ascites grades in patients with liver cirrhosis using laboratory and clinical data: design and implementation study ; volume:60 ; number:12 ; year:2022 ; pages:1946-1954 ; extent:09 ; Clinical chemistry and laboratory medicine ; 60, Heft 12 (2022), 1946-1954 (gesamt 09)

Beteiligte, Orts- und Zeitangaben

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

Weitere Informationen

• DOI: 10.1515/cclm-2022-0454
• URN: urn:nbn:de:101:1-2022110313271085948451
• Rechteinformation: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Letzte Aktualisierung: 15.08.2025, 07:27 MESZ