Dementia is a state of mind in which the sufferer tends to forget important
data like memories, language, etc.. This is caused due to the brain cells that are
damaged. The damaged brain cells and the intensity of the damage can be detected by
using Magnetic Resonance Imaging. In this process, two extraction techniques, Gray
Level Co-Occurrence Matrix (GLCM) and the Gray Level Run-Length matrix
(GLRM), are used for the clear extraction of data from the image of the brain. Then the
data obtained from the extraction techniques are further analyzed using four machine
learning classifiers named Support Vector Machine (SVM), K-Nearest Neighbor
(KNN), Random Forest (RF), and the combination of two classifiers (SVM+KNN).
The results are further analyzed using a confusion matrix to find accuracy, precision,
TPR/FPR - True and False Positive Rate, and TNR/FNR – True and False Negative
Rate. The maximum accuracy of 93.53% is obtained using the GLRM Feature
Extraction (FE) technique with the combination of the SVM and KNN algorithm.
Keywords: Confusion matrix, Dementia, Extraction techniques, Magnetic resonance imaging.