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dc.contributor.authorHsieh, Sung-Huaien_US
dc.contributor.authorCheng, Po-Hsunen_US
dc.contributor.authorHsieh, Sheau-Lingen_US
dc.contributor.authorChen, Po-Haoen_US
dc.contributor.authorWeng, Yung-Chingen_US
dc.contributor.authorChien, Yin-Hsiuen_US
dc.contributor.authorWang, Zhenyuen_US
dc.contributor.authorLai, Feipeien_US
dc.date.accessioned2017-04-21T06:49:42Z-
dc.date.available2017-04-21T06:49:42Z-
dc.date.issued2009en_US
dc.identifier.isbn978-3-642-00908-2en_US
dc.identifier.issn1860-949Xen_US
dc.identifier.urihttp://hdl.handle.net/11536/134417-
dc.description.abstractThe 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.en_US
dc.language.isoen_USen_US
dc.subjectNewborn Screeningen_US
dc.subjectTandem Mass Spectrometryen_US
dc.subjectSupport Vector Machinesen_US
dc.subjectMethylmalonic Acidemiaen_US
dc.titleA Multi Model Voting Enhancement for Newborn Screening Healthcare Information Systemen_US
dc.typeProceedings Paperen_US
dc.identifier.journalNEW ADVANCES IN INTELLIGENT DECISION TECHNOLOGIESen_US
dc.citation.volume199en_US
dc.citation.spage481en_US
dc.citation.epage+en_US
dc.contributor.department資訊技術服務中心zh_TW
dc.contributor.departmentInformation Technology Services Centeren_US
dc.identifier.wosnumberWOS:000267934600046en_US
dc.citation.woscount0en_US
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