Explainable AI-based Alzheimer's prediction and management using multimodal data.
Sobhana JahanKazi Abu TaherM Shamim KaiserMufti MahmudMd Sazzadur RahmanA S M Sanwar HosenIn-Ho RaPublished in: PloS one (2023)
The performance evaluation demonstrates that the RF classifier has a 10-fold cross-validation accuracy of 98.81% for predicting Alzheimer's disease, cognitively normal, non-Alzheimer's dementia, uncertain dementia, and others. In addition, the study utilized Explainable Artificial Intelligence based on the SHAP model and analyzed the causes of prediction. To the best of our knowledge, we are the first to present this multimodal (Clinical, Psychological, and MRI segmentation data) five-class classification of Alzheimer's disease using Open Access Series of Imaging Studies (OASIS-3) dataset. Besides, a novel Alzheimer's patient management architecture is also proposed in this work.
Keyphrases
- cognitive decline
- artificial intelligence
- mild cognitive impairment
- deep learning
- big data
- machine learning
- cognitive impairment
- high resolution
- magnetic resonance imaging
- electronic health record
- pain management
- healthcare
- case report
- computed tomography
- depressive symptoms
- data analysis
- mass spectrometry
- diffusion weighted imaging
- fluorescence imaging