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Volume 15, Issue 2
ADHD Diagnosis and Recognition Based on Functional Classification

Fei Zheng and Chunzheng Cao

J. Info. Comput. Sci. , 15 (2020), pp. 141-145.

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  • 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.
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@Article{JICS-15-141, author = {Fei Zheng and Chunzheng Cao}, title = {ADHD Diagnosis and Recognition Based on Functional Classification}, journal = {Journal of Information and Computing Science}, year = {2024}, volume = {15}, number = {2}, pages = {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. }, issn = {1746-7659}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/jics/22389.html} }
TY - JOUR T1 - ADHD Diagnosis and Recognition Based on Functional Classification AU - Fei Zheng and Chunzheng Cao JO - Journal of Information and Computing Science VL - 2 SP - 141 EP - 145 PY - 2024 DA - 2024/01 SN - 15 DO - http://doi.org/ UR - https://global-sci.org/intro/article_detail/jics/22389.html KW - ADHD, functional classification, FGLM, FLDA, FPCA. AB - 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.
Fei Zheng and Chunzheng Cao. (2024). ADHD Diagnosis and Recognition Based on Functional Classification. Journal of Information and Computing Science. 15 (2). 141-145. doi:
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