Accurate Prediction of Advanced Liver Fibrosis Using the Decision Tree Learning Algorithm in Chronic Hepatitis C Egyptian Patients

@article{Hashem2016AccuratePO,
  title={Accurate Prediction of Advanced Liver Fibrosis Using the Decision Tree Learning Algorithm in Chronic Hepatitis C Egyptian Patients},
  author={Somaya Hashem and G. Esmat and W. Elakel and Shahira M. Habashy and Safaa Abdel Raouf and Samar Kamal Darweesh and M. Soliman and Mohamed Elhefnawi and M. El-Adawy and M. ElHefnawi},
  journal={Gastroenterology Research and Practice},
  year={2016},
  volume={2016}
}
Background/Aim. Respectively with the prevalence of chronic hepatitis C in the world, using noninvasive methods as an alternative method in staging chronic liver diseases for avoiding the drawbacks of biopsy is significantly increasing. The aim of this study is to combine the serum biomarkers and clinical information to develop a classification model that can predict advanced liver fibrosis. Methods. 39,567 patients with chronic hepatitis C were included and randomly divided into two separateโ€ฆย Expand
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References

SHOWING 1-10 OF 34 REFERENCES
A simple noninvasive index can predict both significant fibrosis and cirrhosis in patients with chronic hepatitis C
  • 3,019
Identification of chronic hepatitis C patients without hepatic fibrosis by a simple predictive model
  • 1,025
  • PDF
Identification of the risk for liver fibrosis on CHB patients using an artificial neural network based on routine and serum markers
  • 23
  • PDF
Development of a simple noninvasive index to predict significant fibrosis in patients with HIV/HCV coinfection
  • 2,058
Utility of a discriminant score for diagnosing advanced fibrosis or cirrhosis in patients with chronic hepatitis C virus infection.
  • 256
Performance of serum marker panels for liver fibrosis in chronic hepatitis C.
  • 231
Prospective non-invasive follow-up of liver fibrosis in patients with chronic hepatitis C.
  • 17
  • PDF
Liver biopsy: the best, not the gold standard.
  • 235
  • PDF
Artificial neural network aided non-invasive grading evaluation of hepatic fibrosis by duplex ultrasonography
  • 13
  • PDF
Accurate Prediction of Response to Interferon-based Therapy in Egyptian Patients with Chronic Hepatitis C Using Machine-learning Approaches
  • M. ElHefnawi, M. Abdalla, +5 authors M. Hassan
  • Computer Science
  • 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
  • 2012
  • 14
  • PDF
...
1
2
3
4
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