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• W4205657150 abstract "<sec> <title>BACKGROUND</title> Liver disease is one of the leading causes of death worldwide. Chronic liver disease may lead to cirrhosis of the liver in which the main complications of cirrhosis include ascites. All patients, even in cases of suspected cirrhosis, need to be examined for the causes of ascites. Patients with cirrhosis and ascites often go to transplant centers for evaluation of liver transplants and, therefore, there is a need for accurate prognostic models for the diagnosis of ascites, especially in patients with cirrhosis and ascites. </sec> <sec> <title>OBJECTIVE</title> The aim of the study was to implement a non-invasive model to predict ascites grades among patients with cirrhosis. </sec> <sec> <title>METHODS</title> 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. </sec> <sec> <title>RESULTS</title> 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%. </sec> <sec> <title>CONCLUSIONS</title> We applied well-known ML approaches to predict ascites that the findings showed a strong performance in comparison with the classical statistical approaches. This ML-based approach can help to avoid unnecessary risks and costs for patients with acute stages of the disease. </sec>" @default.
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• W4205657150 date "2022-01-17" @default.
• W4205657150 modified "2023-09-28" @default.
• W4205657150 title "Machine Learning–Based Detection System for ascites in patients with liver cirrhosis Using Laboratory and Clinical Data: Design and Implementation Study (Preprint)" @default.
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• W4205657150 doi "https://doi.org/10.2196/preprints.36567" @default.
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