Machine Learning for Predicting Progression of Alzheimer’s Disease
Typ
Examensarbete för masterexamen
Program
Publicerad
2020
Författare
Egilsdóttir, Hildur
Valur Dansson, Hákon
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
In Alzheimer’s disease (AD), amyloid- (A ) peptides aggregate in the brain forming
plaques. Strangely, these plaques are persistent in both severely cognitively
impaired and cognitively normal individuals. Therefore, it is of big value to investigate
whether other factors cause some patients with A plaques to have AD
dementia and others not. We used data from The Alzheimer’s Disease Neuroimaging
Initiative (ADNI) to study the differences in individuals with evidence of A
plaques and those without. Furthermore, we tried to predict how the cognitive ability
of individuals with plaques would progress in the next four years using machine
learning techniques. Random forest and elastic net estimators were created, predicting
the decline in cognitive test scores as well as diagnosis change of patients only
using data from their first visit. The best regression models, predicting the change
in cognitive test scores achieved R2 scores of 0.428 to 0.580 while the classification
models, predicting whether a patient will get a worse diagnosis achieved a weighted
F1 score of 0.817. Moreover, patients with A plaques seem to decline faster than
those without. The most important features for predicting future cognitive decline
were cognitive tests indicating that already cognitive impaired individuals would
deteriorate more. Other important factors were fluorodeoxyglucose (FDG) obtained
from positron emission tomography and proteins measured in cerebrospinal fluid.
These models could possibly, with further development, be used in clinical settings
as an aid for evaluating how the cognitive function of an individual with A plaques
will develop in the near future.
Beskrivning
Ämne/nyckelord
computer science , machine learning , Alzheimer’s disease , random forest , elastic net , engineering , project , thesis