A Multi-Voting Enhancement for Newborn Screening Healthcare Information System

Sung-Hua Hsieh, Po Hsun Cheng, Chi-Huang Chen, Kuo-Hsuan Huang, Po Hao Chen, Yung-Ching Weng, Sheau-Ling Hsieh, Feipei Lai

Research output: Contribution to journalArticlepeer-review

4 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 essential, imperative to prevent neonatal from these damages. In the paper, we establish a newborn screening model that utilizes Support Vector Machines (SVM) techniques and enhancements to evaluate, interpret the Methylmalonic Acidemia (MMA) metabolic disorders. The model encompasses the Feature Selections, Grid Search, Cross Validations as well as multi model Voting Mechanism. In the model, the predicting accuracy, sensitivity and specificity of MMA can be improved dramatically. The model will be able to apply to other metabolic diseases as well.
Original languageEnglish
Pages (from-to)727-733
Number of pages7
JournalJournal of Medical Systems
Issue number4
StatePublished - Aug 2010


  • Newborn screening; Tandem mass spectrometry; Support vector machines; Methylmalonic acidemia

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