Banu PKN, Inbarani HH, Azar AT, Hala S. Own HS, Hassanien AE. Rough Set Based Feature Selection for Egyptian Neonatal Jaundice (2014). In: A.E. Hassanien, M.F. Tolba, A.T. Azar (eds.), Advanced Machine Learning Technologies and Applications, Communications in Computer and Information Science, Vol. 488, pp. 367-378, Springer-Verlag GmbH Berlin/Heidelberg. DOI: 10.1007/978-3-319-13461-1_35
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This paper analyses rough set based feature selection methods for early intervention and prevention of neurological dysfunction and kernicterus that are the major causes of neonatal jaundice. Newborn babies develop some degree of jaundice which requires high medical attention. Improper prediction of diseases may lead to choose unsuitable type of treatment. Traditional rough set based feature selection methods and tolerance rough set based feature selection methods for supervised and unsupervised approach is applied for Egyptian neonatal jaundice dataset. Features responsible for prediction of Egyptian neonatal jaundice is analyzed using supervised quick reduct, supervised entropy based reduct and Unsupervised Tolerance Rough Set based Quick Reduct (U-TRS-QR). Results obtained demonstrate features selected by U-TRS-QR are highly accurate and will be helpful for physicians for early diagnosis.