Background: Alzheimer’s disease (AD) is the most dominant type of dementia that
has not been treated completely yet. Few Alzheimer‘s patients are correctly diagnosed
on time. Therefore, diagnostic tools are needed for better and more efficient diagnoses.
Objective: This study aimed to develop an efficient automated method to differentiate
Alzheimer’s patients from normal elderly and present the essential features with
accurate Alzheimer’s diagnosis.
Material and Methods: In this analytical study, 154 Magnetic Resonance
Imaging (MRI) scans were obtained from the Alzheimer’s Disease Neuroimaging Initiative
(ADNI) database, preprocessed, and normalized by the head size for extracting
features (volume, cortical thickness, Sulci depth, and Gyrification Index Features
(GIF). Relief-F algorithm, t-test, and one way-ANOVA were used for feature ranking
to obtain the most effective features representing the AD for the classification process.
Finally, in the classification step, four classifiers were used with 10folds cross-validation
as follows: Gaussian Support Vector Machine (GSVM), Linear Support Vector
Machine (LSVM), Weighted K-Nearest Neighbors (W-KNN), and Decision Tree
algorithm.
Results: The LSVM classifier and W-KNN produce a testing accuracy of 100%
with only seven features. Additionally, GSVM and decision tree produce a testing accuracy
of 97.83 % and 93.48 %, respectively.
Conclusion: The proposed system represents an automatic and highly accurate
AD detection with a few reliable and effective features and minimum time.