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


Authors : Govind Kumar Mishra ; Himanshu Maurya ; Nikhil Upadhyay ; Raj Vardhan Chauhan

Volume/Issue : Volume 10 - 2025, Issue 5 - May

Google Scholar : https://tinyurl.com/u4rm3hjy

DOI : https://doi.org/10.38124/ijisrt/25may303

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Abstract : 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.

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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.

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