  Alzheimer's Disease (AD) ravages the cognitive ability of more than 5 million
Americans and creates an enormous strain on the health care system. This paper
proposes a machine learning predictive model for AD development without medical
imaging and with fewer clinical visits and tests, in hopes of earlier and
cheaper diagnoses. That earlier diagnoses could be critical in the
effectiveness of any drug or medical treatment to cure this disease. Our model
is trained and validated using demographic, biomarker and cognitive test data
from two prominent research studies: Alzheimer's Disease Neuroimaging
Initiative (ADNI) and Australian Imaging, Biomarker Lifestyle Flagship Study of
Aging (AIBL). We systematically explore different machine learning models,
pre-processing methods and feature selection techniques. The most performant
model demonstrates greater than 90% accuracy and recall in predicting AD, and
the results generalize across sub-studies of ADNI and to the independent AIBL
study. We also demonstrate that these results are robust to reducing the number
of clinical visits or tests per visit. Using a metaclassification algorithm and
longitudinal data analysis we are able to produce a "lean" diagnostic protocol
with only 3 tests and 4 clinical visits that can predict Alzheimer's
development with 87% accuracy and 79% recall. This novel work can be adapted
into a practical early diagnostic tool for predicting the development of
Alzheimer's that maximizes accuracy while minimizing the number of necessary
diagnostic tests and clinical visits.
