Authors:
Sofia Lahrichi
;
Maryem Rhanoui
;
Mounia Mikram
and
Bouchra El Asri
Affiliation:
IMS Team, ADMIR Laboratory, Rabat IT Center, ENSIAS, Mohammed V University, Rabat, Morocco
Keyword(s):
Alzheimer’s Disease, Multimodal Multitask Learning, Machine Learning, Deep Learning, Progression Detection, Time Series.
Abstract:
Recent studies on modelling the progression of Alzheimer’s disease use a single modality for their predictions while ignoring the time dimension. However, the nature of patient data is heterogeneous and time dependent which requires models that value these factors in order to achieve a reliable diagnosis, as well as making it possible to track and detect changes in the progression of patients’ condition at an early stage. This article overviews various categories of models used for Alzheimer’s disease prediction with their respective learning methods, by establishing a comparative study of early prediction and detection Alzheimer’s disease progression. Finally, a robust and precise detection model is proposed.