ADHD Diagnosis and Recognition Based on Functional Classification
Year: 2020
Journal of Information and Computing Science, Vol. 15 (2020), Iss. 2 : pp. 141–145
Abstract
This research starts from the lack of reliable and effective disease identification biomarkers for attention deficit hyperactivity disorder (ADHD). Based on the functional classification methods, including functional generalized linear model (FGLM), functional linear discriminant analysis (FLDA) method and functional principal component analysis (FPCA), we establish models of corpus callosum (CC) shape and give some analyses. The purpose is to verify whether the corpus callosum shape data can be used as an effective classification basis for disease discrimination and classification, and to provide a new auxiliary discriminant diagnosis idea for ADHD disease discrimination.
Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/2024-JICS-22389
Journal of Information and Computing Science, Vol. 15 (2020), Iss. 2 : pp. 141–145
Published online: 2020-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 5