Abstract
BackgroundThis 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. MethodsBased 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. ResultsThe 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 resonance imaging, and positron emission tomography data were most informative. The best single feature was the functional activities questionnaire. ConclusionExtensive multimodal and incomplete data can be adequately handled by a combination of missing data substitution, feature selection, and classification.
Highlights
Alzheimer’s disease (AD) is the most common cause for dementia in the elderly and primarily diagnosed based on clinical symptoms such as memory loss and disorientation [1]
Recent studies tried to solve this task by using a combination of biomarkers, e.g. obtained via positron emission tomography (PET) or magnetic resonance imaging (MRI), and algorithms adopted from machine learning [3,4,5]
The conversion to AD was predicted with classification accuracies varying between 61.48% and 73.44% based on all features and feature subsets determined by either experts, F-score or forward/backward feature selection
Summary
Alzheimer’s disease (AD) is the most common cause for dementia in the elderly and primarily diagnosed based on clinical symptoms such as memory loss and disorientation [1]. Because not all MCI patients convert to AD and the MCI group is very heterogeneous, it is a highly relevant task to differentiate MCI subjects who will develop AD within the years from those who will be stable or even improve. Computer-based decision support systems are assumed to be more sensitive for the detection of early disease states, and more objective and reliable than medical decisions made by single clinicians [6].
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