Signal discrimination using a support vector machine for genetic syndrome diagnosis

Amit David, Boaz Lerner

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

1 Scopus citations

Abstract

In this study, a support vector machine (SVM) classifies real world data of cytogenetic signals measured from fluorescence in-situ hybridization (FISH) images in order to diagnose genetic syndromes. The study implements the SVM structural risk minimization concept in searching for the optimal setting of the classifier kernel and parameters. We propose thresholding the distance of tested patterns from the SVM separating hyperplane as a way of rejecting a percentage of the miss-classified patterns thereby allowing reduction of the expected risk. Results show accurate performance of the SVM in classifying FISH signals in comparison to other state-of-the-art machine learning classifiers, indicating the potential of an SVM-based genetic diagnosis system.

Original languageEnglish
Title of host publicationProceedings of the 17th International Conference on Pattern Recognition, ICPR 2004
EditorsJ. Kittler, M. Petrou, M. Nixon
Pages490-493
Number of pages4
Volume3
DOIs
StatePublished - 20 Dec 2004
EventProceedings of the 17th International Conference on Pattern Recognition, ICPR 2004 - Cambridge, United Kingdom
Duration: 23 Aug 200426 Aug 2004

Conference

ConferenceProceedings of the 17th International Conference on Pattern Recognition, ICPR 2004
Country/TerritoryUnited Kingdom
CityCambridge
Period23/08/0426/08/04

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