Abstract (EN):
Crohn's disease (CD) diagnosis is a tremendously
serious health problem due to its ultimately effect
on the gastrointestinal tract that leads to the need of complex
medical assistance. In this study, the backpropagation
neural network fuzzy classifier and a neuro-fuzzy model
are combined for diagnosing the CD. Factor analysis is
used for data dimension reduction. The effect on the system
performance has been investigated when using fuzzy
partitioning and dimension reduction. Additionally, further
comparison is done between the different levels of the
fuzzy partition to reach the optimal performance accuracy
level. The performance evaluation of the proposed system
is estimated using the classification accuracy and other
metrics. The experimental results revealed that the classification
with level-8 partitioning provides a classification
accuracy of 97.67 %, with a sensitivity and specificity of
96.07 and 100 %, respectively.
Language:
English
Type (Professor's evaluation):
Scientific
No. of pages:
15