  Alzheimer's disease (AD) and Parkinson's disease (PD) are the two most common
neurodegenerative disorders in humans. Because a significant percentage of
patients have clinical and pathological features of both diseases, it has been
hypothesized that the patho-cascades of the two diseases overlap. Despite this
evidence, these two diseases are rarely studied in a joint manner. In this
paper, we utilize clinical, imaging, genetic, and biospecimen features to
cluster AD and PD patients into the same feature space. By training a machine
learning classifier on the combined feature space, we predict the disease stage
of patients two years after their baseline visits. We observed a considerable
improvement in the prediction accuracy of Parkinson's dementia patients due to
combined training on Alzheimer's and Parkinson's patients, thereby affirming
the claim that these two diseases can be jointly studied.
