Bibliographic description:
Smailova, Sevil. Modeling and Prediction of Alzheimer’s Disease Progression / Sevil Smailova; Supervisor: Dr. Ihor Koval; Ukrainian Catholic University, Department of Computer Sciences. – Lviv : [s.n.], 2021. – 32 p.: ill.
Abstract:
Alzheimer’s Disease is an irreversible disease that causes a decline in cognitive
abilities and leads to dementia. Many efforts are applied to understand the behavior
of the disease progression and foresee its future state. The metrics that assess the
level of cognition are named as cognitive scores. The dynamics of cognitive scores
help understand the future disease progression. However, there is a lack of understanding
on what is the best benchmark for the predicted value of the cognitive
score. Moreover, there could be cases when the future value of the cognitive score is
not statistically different comparing to the current value.
In this work we discover those patients that by design cannot have the dynamics
in their progression of cognitive scores. We justify that the dynamics of progression
for Cognitively Normal patients do not change over five years. We reveal that there
is no statistically significant change in progression after the 1-year follow-ups. We
unified the evaluation framework of different imputation, feature selection methods
and machine learning models on different time to prediction settings as well as on
different patient populations.
