Modeling and Prediction of Alzheimer’s Disease Progression

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.