@inproceedings{ee669c7286e8425cabf72179891b27f3,
title = "A novel model based on non invasive methods for prediction of liver fibrosis",
abstract = "Serial liver biopsies are typically the gold standard for diagnosis of liver fibrosis progression. However, It is associated with serious complications, inconvenient to patients and expensive, the challenge is to substitute the liver biopsy with non-invasive method. The proposed technique is employed to resolve this issue with average accuracy 99.48\% for 5-folds cross validation. This accuracy pave the way to utilize classification models as a clinically non-invasive and reliable method to assess the degree of liver fibrosis.",
keywords = "Classification, Knowledge representation, Minimal unique rules, Subsumption",
author = "Mahmoud Nasr and Khaled El-Bahnasy and M. Hamdy and Kamal, \{Sanaa M.\}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 13th International Computer Engineering Conference, ICENCO 2017 ; Conference date: 27-12-2017 Through 28-12-2017",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/ICENCO.2017.8289800",
language = "English",
series = "ICENCO 2017 - 13th International Computer Engineering Conference: Boundless Smart Societies",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "276--281",
booktitle = "ICENCO 2017 - 13th International Computer Engineering Conference",
address = "United States",
}