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.
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Yin, Z., Zhou, X., Wang, H., Sun, Y., Wong, S.T.C. (2007). Detecting Biomarkers for Major Adverse Cardiac Events Using SVM with PLS Feature Selection and Extraction. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72393-6_130
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DOI: https://doi.org/10.1007/978-3-540-72393-6_130
Publisher Name: Springer, Berlin, Heidelberg
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