  Nowadays, a lot of scientific efforts are concentrated on the diagnosis of
Alzheimer's Disease (AD) applying deep learning methods to neuroimaging data.
Even for 2017, there were published more than a hundred papers dedicated to AD
diagnosis, whereas only a few works considered a problem of mild cognitive
impairments (MCI) conversion to the AD. However, the conversion prediction is
an important problem since approximately 15% of patients with MCI converges to
the AD every year. In the current work, we are focusing on the conversion
prediction using brain Magnetic Resonance Imaging and clinical data, such as
demographics, cognitive assessments, genetic, and biochemical markers. First of
all, we applied state-of-the-art deep learning algorithms on the neuroimaging
data and compared these results with two machine learning algorithms that we
fit using the clinical data. As a result, the models trained on the clinical
data outperform the deep learning algorithms applied to the MR images. To
explore the impact of neuroimaging further, we trained a deep feed-forward
embedding using similarity learning with Histogram loss on all available MRIs
and obtained 64-dimensional vector representation of neuroimaging data. The use
of learned representation from the deep embedding allowed to increase the
quality of prediction based on the neuroimaging. Finally, the current results
on this dataset show that the neuroimaging does affect conversion prediction,
however, cannot noticeably increase the quality of the prediction. The best
results of predicting MCI-to-AD conversion are provided by XGBoost algorithm
trained on the clinical and embedding data. The resulting accuracy is 0.76 +-
0.01 and the area under the ROC curve - 0.86 +- 0.01.
