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 separate… Expand
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