Abstract:
BACKGROUND:Liver cirrhosis (LC) is the final stage of most of chronic liver diseases and is almost caused by chronic hepatitis B (CHB) in China. Liver biopsy is the reference method for the evaluation of liver cirrhosis. However, it is an invasive procedure with inherent risk. The aim of this study was to construct a new classifier based on the routine clinical markers for the prediction of HBV-induced LC. SUBJECTS AND METHODS:We collected routine clinical parameters from 124 LC patients with CHB and 115 with CHB. Training set (n = 120) and test set (n = 119) were built for model construction and evaluation, respectively. RESULTS:We describe a new classifier, MLP, for prediction of LC with CHB. MLP was built with seven routinely available clinical parameters, including age, ALT, AST, PT, PLT, HGB, and RDW. With optimal cutoff, we obtained a sensitivity of 95.2%, a specificity of 84.2%, and an overall accuracy of 89.9% on an independent test set, which were superior to those of FIB-4 and APRI. CONCLUSIONS:Our study suggests that the MLP classifier can be implemented for discriminating LC and non-LC cohorts by using machine learning method based on the routine available clinical parameters. It could be used for clinical practice in HBV-induced LC assessment.
journal_name
Dis Markersjournal_title
Disease markersauthors
Cao Y,Hu ZD,Liu XF,Deng AM,Hu CJdoi
10.1155/2013/127962subject
Has Abstractpub_date
2013-01-01 00:00:00pages
653-60issue
6eissn
0278-0240issn
1875-8630journal_volume
35pub_type
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