Detecting biomarkers for major adverse cardiac events using SVM with PLS feature selection and extraction

Zheng Yin, Xiaobo Zhou, Honghui Wang, Youxian Sun, Stephen T C Wong

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

Abstract

Detection of biomarkers capable of predicting a patient's risk of major adverse cardiac events (MACE) is of clinical significance. Due to the high dynamic range of the protein concentration in human blood, applying proteomics techniques for protein profiling can generate large arrays of data for development of optimized clinical biomarker panels. The objective of this study is to discover an optimized subset of biomarkers for predicting risk of MACE containing less than ten biomarkers. In this paper, we connect linear SVM with PLS feature selection and extraction. A simplified PLS algorithm selects a subset of biomarkers and extracts latent variables and prediction performance of linear SVM is dramatically improved. The proposed method is compared with a widely used PLS-Logistic Discriminant solution and several other reported methods based on the MACE prediction experiments.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Networks - ISNN 2007 - 4th International Symposium on Neural Networks, ISNN 2007, Proceedings
PublisherSpringer-Verlag
Pages1097-1106
Number of pages10
EditionPART 2
ISBN (Print)9783540723929
DOIs
StatePublished - 2007
Event4th International Symposium on Neural Networks, ISNN 2007 - Nanjing, China
Duration: Jun 3 2007Jun 7 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume4492 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other4th International Symposium on Neural Networks, ISNN 2007
CountryChina
CityNanjing
Period6/3/076/7/07

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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