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Alzheimer's Disease Diagnosis via Deep Factorization Machine Models
The current state-of-the-art deep neural networks (DNNs) for Alzheimer's Disease diagnosis use different biomarker combinations to classify patients, but do not allow extracting knowledge about the interactions of biomarkers.
Deep Learning in Alzheimer's disease: Diagnostic Classification and Prognostic Prediction using Neuroimaging Data
The combination of traditional machine learning for classification and stacked auto-encoder (SAE) for feature selection produced accuracies of up to 98. 8% for AD classification and 83. 7% for prediction of conversion from mild cognitive impairment (MCI), a prodromal stage of AD, to AD.
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