Application of fuzzy logic for Alzheimer's disease diagnosis

Fuzzy Inference System (FIS) is developed using subtractive clustering algorithm, and applied to classification between MRI images of patients having Mild Cognitive Impairment (MCI) or Alzheimer's Disease (AD) and Normal Controls (NC). Features used as FIS inputs are mean values and standard de...

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Bibliographic Details
Published in2015 Signal Processing Symposium (SPSympo) pp. 1 - 4
Main Authors Krashenyi, Igor, Popov, Anton, Ramirez, Javier, Gorriz, Juan Manuel
Format Conference Proceeding
LanguageEnglish
Published Warsaw University of Technology 01.06.2015
Subjects
Alzheimer's disease
classification
Fuzzy logic
Magnetic resonance imaging
mild-cognitive impairment
MRI
Support vector machines
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Summary:Fuzzy Inference System (FIS) is developed using subtractive clustering algorithm, and applied to classification between MRI images of patients having Mild Cognitive Impairment (MCI) or Alzheimer's Disease (AD) and Normal Controls (NC). Features used as FIS inputs are mean values and standard deviations in intensities from most descriptive brain regions. k-fold cross-validation was used to estimate FIS performance, resulting in accuracy, sensitivity, specificity and positive predictive value (ppv) characteristics of FIS classification between different groups. ppv was equal to 0.8778±0.0088 (AD vs. NC), 0.7289±0.0243 (NC vs. MCI), and 0.8531±0.0069 (MCI vs. AD).
DOI:10.1109/SPS.2015.7168288