Newborn Screening for Phenylketonuria: Machine Learning vs Clinicians

Wei-Hsin Chen, Han-Ping Chen, Yi-Ju Tseng, Kai-Ping Hsu, Sheau-Ling Hsieh, Yin-Hsiu Chien, Wuh-Liang Hwu, Feipei Lai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

The metabolic disorders may hinder an infant's normal physical or mental development during the neonatal period. The metabolic diseases can be treated by effective therapies if the diseases are discovered in the early stages. Therefore, newborn screening program is essential to prevent neonatal from these damages. In the paper, a support vector machine (SVM) based algorithm is introduced in place of cut-off value decision to evaluate the analyte elevation raw data associated with Phenylketonuria. The data were obtained from tandem mass spectrometry (MS/MS) for newborns. In addition, a combined feature selection mechanism is proposed to compare with the cut-off scheme. By adapting the mechanism, the number of suspected cases is reduced substantially; it also handles the medical resources effectively and efficiently.
Original languageEnglish
Title of host publicationIEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
PublisherIEEE
Pages798-803
Number of pages6
DOIs
StatePublished - 2012

Keywords

  • Newborn screening; Tandem mass spectrometry; Support Vector Machine
  • CUTOFF VALUES; STRATEGIES; AGE

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    Chen, W-H., Chen, H-P., Tseng, Y-J., Hsu, K-P., Hsieh, S-L., Chien, Y-H., Hwu, W-L., & Lai, F. (2012). Newborn Screening for Phenylketonuria: Machine Learning vs Clinicians. In IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (pp. 798-803). IEEE. https://doi.org/10.1109/ASONAM.2012.145