Modeling autosomal dominant Alzheimer's disease with machine learning.

ABSTRACT:

Introduction

Machine learning models were used to discover novel disease trajectories for autosomal dominant Alzheimer's disease.

Methods

Longitudinal structural magnetic resonance imaging, amyloid positron emission tomography (PET), and fluorodeoxyglucose PET were acquired in 131 mutation carriers and 74 non-carriers from the Dominantly Inherited Alzheimer Network; the groups were matched for age, education, sex, and apolipoprotein ε4 (APOE ε4). A deep neural network was trained to predict disease progression for each modality. Relief algorithms identified the strongest predictors of mutation status.

Results

The Relief algorithm identified the caudate, cingulate, and precuneus as the strongest predictors among all modalities. The model yielded accurate results for pr

SUBMITTER: Luckett PH 

PROVIDER: S-EPMC8195816 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature