Detecting Biomarkers for Major Adverse Cardiac Events Using SVM with PLS Feature Selection and Extraction

Authors:
Zheng Yin;Xiaobo Zhou;Honghui Wang;Youxian Sun;Stephen T. Wong
Affiliations:
National Laboratory of Industrial Control Technology, Institute of Industrial Process Control, Zhejiang University, Hangzhou, 310027, P.R. China;Functional and Molecular Imaging Center, Brigham and Women's Hospital, Boston, MA 02121, USA and HCNR Center for Bioinformatics, Harvard Medical School, Boston, MA 02115, USA;Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD 20892, USA;National Laboratory of Industrial Control Technology, Institute of Industrial Process Control, Zhejiang University, Hangzhou, 310027, P.R. China;Functional and Molecular Imaging Center, Brigham and Women's Hospital, Boston, MA 02121, USA and HCNR Center for Bioinformatics, Harvard Medical School, Boston, MA 02115, USA
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Year:
2007

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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.