Voxel Extraction and Multiclass Classification of Identified Brain Regions across Various Stages of Alzheimer’s Disease Using Machine Learning Approaches

• Published in: Diagnostics (Basel) Vol. 13; no. 18; p. 2871
• Main Authors: Shahzadi, Samra, Butt, Naveed Anwer, Sana, Muhammad Usman, Pascual, Iñaki Elío, Urbano, Mercedes Briones, Díez, Isabel de la Torre, Ashraf, Imran
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2023; MDPI
• Subjects: Accuracy; Advertising executives; Algorithms; Alzheimer's disease; Alzheimer’s disease detection; Automation; Brain; Brain research; Classification; Cognitive ability; Data mining; Discriminant analysis; Feature selection; Generalized linear models; Machine learning; Memory; Neural networks; Principal components analysis; Regression analysis; Support vector machines; Wavelet transforms
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics13182871

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.