TY  - JOUR
SN  - 2075-4418
UR  - http://doi.org/10.3390/diagnostics13182871
ID  - uninimx8725
TI  - Voxel Extraction and Multiclass Classification of Identified Brain Regions across Various Stages of Alzheimer?s Disease Using Machine Learning Approaches
N2  - This study sought to investigate how different brain regions are affected by Alzheimer?s disease (AD) at various phases of the disease, using independent component analysis (ICA). The study examines six regions in the mild cognitive impairment (MCI) stage, four in the early stage of Alzheimer?s disease (AD), six in the moderate stage, and six in the severe stage. The precuneus, cuneus, middle frontal gyri, calcarine cortex, superior medial frontal gyri, and superior frontal gyri were the areas impacted at all phases. A general linear model (GLM) is used to extract the voxels of the previously mentioned regions. The resting fMRI data for 18 AD patients who had advanced from MCI to stage 3 of the disease were obtained from the ADNI public source database. The subjects include eight women and ten men. The voxel dataset is used to train and test ten machine learning algorithms to categorize the MCI, mild, moderate, and severe stages of Alzheimer?s disease. The accuracy, recall, precision, and F1 score were used as conventional scoring measures to evaluate the classification outcomes. AdaBoost fared better than the other algorithms and obtained a phenomenal accuracy of 98.61%, precision of 99.00%, and recall and F1 scores of 98.00% each.
A1  - Shahzadi, Samra
A1  - Butt, Naveed Anwer
A1  - Sana, Muhammad Usman
A1  - Elío Pascual, Iñaki
A1  - Briones Urbano, Mercedes
A1  - Díez, Isabel de la Torre
A1  - Ashraf, Imran
VL  - 13
AV  - public
JF  - Diagnostics
KW  - Alzheimer?s disease detection; classification; machine learning
Y1  - 2023/09//
IS  - 18
ER  -