Computer aided progression detection model based on optimized deep LSTM ensemble model and the fusion of multivariate time series data.

ABSTRACT: Alzheimer's disease (AD) is the most common form of dementia. Early and accurate detection of AD is crucial to plan for disease modifying therapies that could prevent or delay the conversion to sever stages of the disease. As a chronic disease, patient's multivariate time series data including neuroimaging, genetics, cognitive scores, and neuropsychological battery provides a complete profile about patient's status. This data has been used to build machine learning and deep learning (DL) models for the early detection of the disease. However, these models still have limited performance and are not stable enough to be trusted in real medical settings. Literature shows that DL models outperform classical machine learning models, but ensemble learning has proven to achieve better results than

SUBMITTER: Saleh H 

PROVIDER: S-EPMC10539296 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature