Computer Aided Progression Detection Model Based On Optimized Deep Lstm Ensemble Model And The Fusion Of Multivariate Time Series Data

This article discusses the use of machine learning to diagnose and predict Alzheimer’s Disease (AD). It proposes a novel deep learning model based on a group of Long Short-Term Memory (LSTM) base classifiers to interpret time series data collected from the National Alzheimer’s Coordinating Center (NACC) dataset. This model is designed to accurately predict AD based on a collection of cost-effective multivariate time series data and uses the Bayesian optimizer technique to build an optimal deep stacking model.