Pattern classification using a support vector machine for genetic disease diagnosis

Amit David, Boaz Lerner

Research output: Contribution to conferencePaperpeer-review

6 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
Pages289-292
Number of pages4
StatePublished - 1 Dec 2004
Event2004 23rd IEEE Convention of Electrical and Electronics Engineers in Israel, Proceedings - Tel-Aviv, Israel
Duration: 6 Sep 20047 Sep 2004

Conference

Conference2004 23rd IEEE Convention of Electrical and Electronics Engineers in Israel, Proceedings
Country/TerritoryIsrael
CityTel-Aviv
Period6/09/047/09/04

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