Multimodal prediction of conversion to Alzheimer's disease based on incomplete biomarkers.

ABSTRACT:

Background

This study investigates the prediction of mild cognitive impairment-to-Alzheimer's disease (MCI-to-AD) conversion based on extensive multimodal data with varying degrees of missing values.

Methods

Based on Alzheimer's Disease Neuroimaging Initiative data from MCI-patients including all available modalities, we predicted the conversion to AD within 3 years. Different ways of replacing missing data in combination with different classification algorithms are compared. The performance was evaluated on features prioritized by experts and automatically selected features.

Results

The conversion to AD could be predicted with a maximal accuracy of 73% using support vector machines and features chosen by experts. Among data modalities, neuropsychological, magnetic re

SUBMITTER: Ritter K 

PROVIDER: S-EPMC4877756 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature