An Extensive Analysis of Alzheimer's Disease: Pathophysiology, Identification and New Treatment Approaches

Alzheimer's disease (AD), the most prevalent cause of dementia worldwide, is a degenerative neurological

condition that poses significant financial and medical challenges. The paper examines how well different machine learning

methods perform in classifying Alzheimer's disease using datasets like sMRI, ADNI, and ADNI+OASIS. The study contrasts

sophisticated deep learning models like 2D-DCNN, CNN-BiLSTM, and VGG16 with more conventional algorithms like SVM

and Random Forest, which achieve accuracies between 85% and 89%. Using MRI data, 2D-DCNN notably gets the

maximum accuracy of 99%, but SVM Multikernel and Multi-class Classification reach 98% and 96%, respectively. The

effectiveness of hybrid techniques is demonstrated by ensemble approaches that integrate MRI with genetic and

demographic data, which achieve accuracies of up to 88%. PET and fMRI have maximum accuracies of 89% and 94%,

respectively, but MRI-based methods routinely do better. With an emphasis on MRI as the primary modality, the research

shows that deep learning models and multimodal data integration greatly improve diagnostic accuracy.