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Brain age prediction using improved twin SVR

  • S.I.: Improving Healthcare outcomes using Multimedia Big Data Analytics
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Abstract

Twin support vector regression (TSVR) has been widely applied in regression problems. TSVR seeks a pair of \(\varepsilon \)-insensitive proximal planes by solving two support vector machine type problems. TSVR assumes that the matrices appearing in the dual formulation are positive definite. However, in real-world scenarios, such an assumption may not be fulfilled and, hence, leads to suboptimal performance. \(\varepsilon \)-Twin support vector regression (\(\varepsilon \)-TSVR) improved the TSVR by introducing the regularisation term to avoid the singularity issues. Most of the twin support vector machine models involve the computation of matrix inverses. Also, TSVR implements the empirical risk minimization principle. In this paper, we propose an improved twin support vector regression (ITSVR) for brain age estimation by introducing different Lagrangian functions for the primal problems of the TSVR. The proposed ITSVR implements the structural risk minimization principle and avoids the computation of the matrix inverses. To solve the optimization problem more efficiently, we used successive overrelaxation (SOR) technique. We evaluated the proposed ITSVR on cognitively healthy subjects, mild cognitive impairment subjects and Alzheimer’s disease subjects. The experimental results demonstrate that the proposed ITSVR has superior performance compared to the baseline models for brain age estimation.

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Acknowledgements

This work is supported by Science and Engineering Research Board (SERB), Government of India under Ramanujan Fellowship Scheme, Grant No. SB/S2/RJN-001/2016, and Council of Scientific & Industrial Research (CSIR), New Delhi, INDIA for funding under Extra Mural Research (EMR) Scheme grant no. 22(0751)/17/EMR-II. We gratefully acknowledge the Indian Institute of Technology Indore for providing the required facilities and support for this work. Besides, this study was performed based on multiple samples of participants. We wish to acknowledge all participants and principal investigators who collected these datasets and agreed to let them accessible: The Open Access Series of Imaging Studies (OASIS), Cross-Sectional, Principal Investigators: D. Marcus, R, Buckner, J, Csernansky J. Morris, P50 AG05681, P01 AG03991, P01 AG026276, R01 AG021910, P20 MH071616, U24 RR021382, see http://www.oasis-brains.org/ for more details. The IXI data were supported by the U.K. Engineering and Physical Sciences Research Council (EPSRC) GR/S21533/02, see http://www.brain-development.org/ for more details.

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Authors and Affiliations

  1. Department of Mathematics, Indian Institute of Technology Indore, Simrol, Indore, 453552, India

    M. A. Ganaie & M. Tanveer

  2. Department of Human Anatomy and Cell Science, Rady Faculty of Health Sciences, Max Rady College of Medicine, University of Manitoba, Winnipeg, MB, Canada

    Iman Beheshti

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  1. M. A. Ganaie
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  2. M. Tanveer
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Contributions

M.T. and M.A.G. designed the research; I.B. collected data and performed pre-processing. M.A.G. performed numerical experiments, analysed data, and wrote the paper. M.T. and I.B. edited the paper. M.T. supervised the study.

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Correspondence to M. Tanveer.

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Ganaie, M.A., Tanveer, M. & Beheshti, I. Brain age prediction using improved twin SVR. Neural Comput & Applic 36, 53–63 (2024). https://doi.org/10.1007/s00521-021-06518-1

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