Brain age prediction using improved twin SVR

• Published in: Neural computing & applications Vol. 36; no. 1; pp. 53 - 63
• Main Authors: Ganaie, M. A., Tanveer, M., Beheshti, Iman
• Format: Journal Article
• Language: English
• Published: London Springer London 01.01.2024; Springer Nature B.V
• Subjects: Artificial Intelligence; Brain; Chronology; Computation; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Image Processing and Computer Vision; Lagrangian function; Mathematical analysis; Optimization; Principles; Probability and Statistics in Computer Science; Regression; Regularization; S.I.: Improving Healthcare outcomes using Multimedia Big Data Analytics; Singularity (mathematics); Special Issue on Improving Healthcare outcomes using Multimedia Big Data Analytics; Support vector machines; Regression; Brain age estimation; Algorithms; T1-weighted MRI; Alzheimer’s disease; Machine learning
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-021-06518-1

Twin support vector regression (TSVR) has been widely applied in regression problems. TSVR seeks a pair of ε -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. ε -Twin support vector regression ( ε -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.