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.