  Time-dependent data collected in studies of Alzheimer's disease usually has
missing and irregularly sampled data points. For this reason time series
methods which assume regular sampling cannot be applied directly to the data
without a pre-processing step. In this paper we use a machine learning method
to learn the relationship between pairs of data points at different time
separations. The input vector comprises a summary of the time series history
and includes both demographic and non-time varying variables such as genetic
data. The dataset used is from the 2017 TADPOLE grand challenge which aims to
predict the onset of Alzheimer's disease using including demographic, physical
and cognitive data. The challenge is a three-fold diagnosis classification into
AD, MCI and control groups, the prediction of ADAS-13 score and the normalised
ventricle volume. While the competition proceeds, forecasting methods may be
compared using a leaderboard dataset selected from the Alzheimer's Disease
Neuroimaging Initiative (ADNI) and with standard metrics for measuring
accuracy. For diagnosis, we find an mAUC of 0.82, and a classification accuracy
of 0.73. The results show that the method is effective and comparable with
other methods.
