Abstract: The most dominant form of dementia, memory loss, is Alzheimer's disease (AD). Imaging is important for monitoring, diagnosis, and education of Alzheimer's disease prediction. Automated classification of subjects could provide support for clinicians. This study examined two classification methods to separate among elderly persons with normal cognitive (NC), Alzheimer's disease (AD), and mild cognitive impairment (MCI) by using images from the magnetic resonance imaging (MRI). The dataset consists of 120 subjects separated into 40 ADs, 40 MCIs, and 40 NCs. The first technique was K-Nearest Neighbor (KNN) and the second technique was Support Vector Machine (SVM), firstly all the subjects were filtered and normalized, secondly twelve features were extracted. After feature selection, two techniques of classification were examined with Permutations and combinations for all features in order to select the best features which have the highest accuracy for identification the classes. The best average accuracy was 97.92% using SVM polynomial order three, and best all average accuracy was 95.833% using KNN with K=6, and K=7 for random selection of testing data with SVM and KNN. The results show a relatively high classification accuracy between the three clinical categories. In summary, the proposed automatic classification technique can be used as a noninvasive diagnostic tool for Alzheimer's disease, with the capability of defining early stages of the disease.Abstract: The most dominant form of dementia, memory loss, is Alzheimer's disease (AD). Imaging is important for monitoring, diagnosis, and education of Alzheimer's disease prediction. Automated classification of subjects could provide support for clinicians. This study examined two classification methods to separate among elderly persons with normal cogniti...Show More
