Newborn Screening System Based on Adaptive Feature Selection and Support Vector Machines

Sung-Huai Hsieh, Yin-Hsiu Chien, Chia-Ping Shen, Wei-Hsin Chen, Po Hao Chen, Sheau-Ling Hsieh, Po-Hsun Cheng, Feipei Lai

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


The clinical symptoms of metabolic disorders during neonatal period are often not apparent, if not treated early irreversible damages such as mental retardation may occur, even death. Therefore, practicing newborn screening is very important to prevent neonatal from these damages. In this paper, the newborn screening system used support vector machines (SVM) classification technique is proposed in place of cut-off value decision to evaluate the metabolic substances concentration raw data obtained from tandem mass spectrometry (MS/MS) and determine whether the newborn has some kinds of metabolic disorder diseases. On the basis of the proposed features, new analytic combinations are identified with superior discriminatory performance compared with the best published combinations. Classifiers built with the feature selection to find C3/C2, C3 and C16 of three key point features achieved diagnostic sensitivities, specificities and accuracy approaching 100%.
Original languageEnglish
Title of host publication9th IEEE International Conference on BioInformatics and BioEngineering
StatePublished - 2009



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