Machine Learning for Predicting Progression of Alzheimer’s Disease

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dc.contributor.authorEgilsdóttir, Hildur
dc.contributor.authorValur Dansson, Hákon
dc.contributor.departmentChalmers tekniska högskola / Institutionen för data och informationstekniksv
dc.contributor.examinerSchliep, Alexander
dc.contributor.supervisorJohansson, Fredrik
dc.contributor.supervisorSchliep, Alexander
dc.date.accessioned2020-09-18T13:23:04Z
dc.date.available2020-09-18T13:23:04Z
dc.date.issued2020sv
dc.date.submitted2020
dc.description.abstractIn 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.sv
dc.identifier.coursecodeDATX05sv
dc.identifier.urihttps://hdl.handle.net/20.500.12380/301732
dc.language.isoengsv
dc.setspec.uppsokTechnology
dc.subjectcomputer sciencesv
dc.subjectmachine learningsv
dc.subjectAlzheimer’s diseasesv
dc.subjectrandom forestsv
dc.subjectelastic netsv
dc.subjectengineeringsv
dc.subjectprojectsv
dc.subjectthesissv
dc.titleMachine Learning for Predicting Progression of Alzheimer’s Diseasesv
dc.type.degreeExamensarbete för masterexamensv
dc.type.uppsokH
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