PCA-SGA implementation in classification and disease specific feature extraction of the brain MRS signals.
The medical diagnostic systems often suffer from the high dimensional data. In this study, Principle Component Analysis (PCA) has been used for dimensionality reduction of the brain Magnetic Resonance Spectroscopy (MRS) signals. Afterwards, the Simple Genetic Algorithms (SGA) is utilized in order to classify different brain diseases. SGA is later used to extract MRS signal features in case of metabolic brain diseases (MD). The PCA-SGA implementation received the specificity of 89.91%. The SGA was able to achieve the sensitivity of 84.84% and positive predictivity of 88.46% in extracting disease specific MRS signal features.
Mahmoodabadi
S Zarei
SZ
Department of Electrical Engineering at Ryerson University, Toronto, ON, Canada. szareima@ryerson.ca
Alirezaie
J
J
Babyn
P
P
Kassner
A
A
Widjaja
E
E
D000465:Algorithms; D001185:Artificial Intelligence; D001927:Brain Diseases / Q000175:diagnosis* / Q000473:pathology*; D001932:Brain Neoplasms / Q000175:diagnosis* / Q000473:pathology*; D002648:Child; D003936:Diagnosis, Computer-Assisted; D016002:Discriminant Analysis; D006801:Humans; D009682:Magnetic Resonance Spectroscopy / Q000295:instrumentation / Q000379:methods*; D011237:Predictive Value of Tests; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D012984:Software