Syncretic Feature Selection for Machine Learning-Aided Prognostics of Hepatitis

Author:

Parisi LucaORCID,RaviChandran Narrendar

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,General Neuroscience,Software

Reference49 articles.

1. Castera L (2012) Noninvasive methods to assess liver disease in patients with hepatitis B or C. Gastroenterology 142(6):1293–1302

2. Salkic NN, Jovanovic P, Hauser G, Brcic M (2014) FibroTest/Fibrosure for significant liver fibrosis and cirrhosis in chronic hepatitis B: a meta-analysis. Am J Gastroenterol 109(6):796–809

3. Gong G (1988) Hepatitis data set. UCI machine learning repository [http:archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Sciences

4. Parisi L, Manaog ML (2016) Preliminary validation of the Lagrangian support vector machine learning classifier as clinical decision-making support tool to aid prediction of prognosis in patients with hepatitis. In The 16th international conference on biomedical engineering, National University of Singapore (NUS)

5. Hansen JV, McDonald JB (2001) Some experimental evidence on the performance of GA-designed neural networks. J Exp Theor Artif Intell 13(3):307–321

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