Abstract
Attention Deficit Hyperactivity Disorder (ADHD) is a common nerobehavioral disease in school-age children. Its accurate diagnostic methods have drawn widespread attention in recent years. Among them, neurobiological diagnosis methods are proved as a significant way to identify ADHD patients. By employing some neurobiological measures of ADHD, machine learning is treated as a useful tool for ADHD diagnosis (or classification). In this work, we develop a Laplacian regularization subspace learning model for ADHD classification. In detail, we use resting-state Functional Connectivities (FCs) of the brain as input neurobiological data and cast them into the subspace learning model which is carried out in an existing binary hypothesis testing framework. In this testing framework, under a hypothesis of the test subject (ADHD or healthy control subject), training data generates its corresponding feature set in the feature selection phase. Then, the feature set is turned to its projected features by the subspace model for each hypothesis. Here, a Laplacian regularization is employed to enhance the relationship of intra-class subjects. By comparing the subspace energies of projection features between two hypotheses, a label is finally predicted for the test subject. Experiments show, on the ADHD-200 database, the average accuracy is about 91.8% for ADHD classification, which outperforms most of the existing machine learning and deep learning methods.
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Acknowledgements
This work is partly supported by Fundamental Research Funds for Central Universities, China, under Grant B200202217; Changzhou Science and Technology Program, China, under Grant CJ20200065 and CE20205043; Changzhou Science and Technology Program, China, under Grant CE20205043.
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Wang, Y., Gao, Y., Jiang, J., Lin, M., Tang, Y. (2021). Subspace Classification of Attention Deficit Hyperactivity Disorder with Laplacian Regularization. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2021. Lecture Notes in Computer Science(), vol 12736. Springer, Cham. https://doi.org/10.1007/978-3-030-78609-0_11
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