ArticleDisease markers2013
An MLP classifier for prediction of HBV-induced liver cirrhosis using routinely available clinical parameters.
Article in Disease markers, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 34 citations in OpenAlex.
- Predictive Cellular Signatures from Live Human Motor Neurons Distinguish TDP-43 ALS and Enable ALS Subtype Stratification.bioRxiv : the preprint server for biology · 2026Article
- Assisting the Diagnosis of Cirrhosis in Chronic Hepatitis C Patients Based on Machine Learning Algorithms: A Novel Non-Invasive Approach.Journal of clinical laboratory analysis · 2025Observational
- Prediction of xerostomia in elderly based on clinical characteristics and salivary flow rate with machine learning.Scientific reports · 2024Article
- Article
- Clinical Characteristics and Outcomes of Nonalcoholic Fatty Liver Disease-Associated Hepatocellular Carcinoma in the United States.Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association · 2023Article
- Clinlabomics: leveraging clinical laboratory data by data mining strategies.BMC bioinformatics · 2022Review
- Comparison between Statistical Models and Machine Learning Methods on Classification for Highly Imbalanced Multiclass Kidney Data.Diagnostics (Basel, Switzerland) · 2020Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLiver 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
methodsWe 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.
resultsWe 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.
conclusionsOur 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.
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