Machine learning-based system for prediction of ascites grades in patients with liver cirrhosis using laboratory and clinical data: design and implementation study
Author:
Hatami Behzad1, Asadi Farkhondeh2, Bayani Azadeh2, Zali Mohammad Reza1, Kavousi Kaveh3
Affiliation:
1. Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences , Tehran , Iran 2. Department of Health Information Technology and Management , School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences , Tehran , Iran 3. Laboratory of Complex Biological Systems and Bioinformatics (CBB), Institute of Biochemistry and Biophysics (IBB), University of Tehran , Tehran , Iran
Abstract
Abstract
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.
Publisher
Walter de Gruyter GmbH
Subject
Biochemistry (medical),Clinical Biochemistry,General Medicine
Reference30 articles.
1. Fedeli, U, Avossa, F, Guzzinati, S, Bovo, E, Saugo, M. Trends in mortality from chronic liver disease. Ann Epidemiol 2014;24:522–6. https://doi.org/10.1016/j.annepidem.2014.05.004. 2. Nayak, A, Kayal, EB, Arya, M, Culli, J, Krishan, S, Agarwal, S, et al.. Computer-aided diagnosis of cirrhosis and hepatocellular carcinoma using multi-phase abdomen CT. Int J Comput Assist Radiol Surg 2019;14:1341–52. https://doi.org/10.1007/s11548-019-01991-5. 3. Aleksić, A, Nedeljković, S, Jovanović, M, Ranđelović, M, Vuković, M, Stojanović, V, et al.. Prediction of important factors for bleeding in liver cirrhosis disease using ensemble data mining approach. Mathematics 2020;8:1887. https://doi.org/10.3390/math8111887. 4. Ibrahim, M, Mostafa, I, Devière, J. New developments in managing variceal bleeding. Gastroenterology 2018;154:1964–9. https://doi.org/10.1053/j.gastro.2018.02.023. 5. Jalan, R, Gines, P, Olson, JC, Mookerjee, RP, Moreau, R, Garcia-Tsao, G, et al.. Acute-on chronic liver failure. J Hepatol 2012;57:1336–48. https://doi.org/10.1016/j.jhep.2012.06.026.
Cited by
9 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|