A new weighted rough set framework based classification for Egyptian NeoNatal Jaundice

Authors:
Hala S. Own;Ajith Abraham
Affiliations:
Department of Solar and Space Research, National Research Institute of Astronomy and Geophysics, El-Marsad Street, P.O. Box 11421, Helwan, Egypt;Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, Washington 98071, USA and VSB - Technical University of Ostrava, 17, listopadu 15/2172, Os ...
Venue:
Applied Soft Computing
Year:
2012

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Abstract

Prediction of diseases would help physicians to make informal decision regarding the type of treatment. Jaundice is the most common condition that requires medical attention in newborn babies. Although most newborns develop some degree of jaundice, a high level bilirubin puts a newborn at risk of bilirubin encephalopathy and kernicterus, which are rare but still occur in Egypt. This paper presents a new weighted rough set framework for early intervention and prevention of neurological dysfunction and kernicterus that are catastrophic sequels of neonatal jaundice. The obtained results illustrate that the weighted rough set can provide significantly more accurate and reliable predictive accuracy than well known algorithms such as weighted SVM and decision tree considering the fact that physicians do not have any estimation about probability of jaundice appearance.