We proposed two stages: classification stage and regression stage, to detect Alzheimer disease progression based on the information fusion of many multivariate time series modalities, including neuroimaging data, cognitive scores, cerebrospinal fluid biomarkers, neuropsychological battery markers, and demographics. In classification stage We applied support vector machine (SVM), random forest (RF), decision tree (DT), k-nearest neighbor (KNN), and logistic regression (LR), and the feedforward neural network and long short-term memory (LSTM)

In regression stage We applied support vector regression, random forest regression, decision tree regression, Ridge, Lasso, and the feedforward neural network and long short-term memory (LSTM)

we applied Feedforward neural network in three-time series: • flattened time series • Baseline time step • M18 time step