  Alzheimer's disease (AD) is a degenerative brain disease impairing a person's
ability to perform day to day activities. The clinical manifestations of
Alzheimer's disease are characterized by heterogeneity in age, disease span,
progression rate, impairment of memory and cognitive abilities. Due to these
variabilities, personalized care and treatment planning, as well as patient
counseling about their individual progression is limited. Recent developments
in machine learning to detect hidden patterns in complex, multi-dimensional
datasets provides significant opportunities to address this critical need. In
this work, we use unsupervised and supervised machine learning approaches for
subtype identification and prediction. We apply machine learning methods to the
extensive clinical observations available at the Alzheimer's Disease
Neuroimaging Initiative (ADNI) data set to identify patient subtypes and to
predict disease progression. Our analysis depicts the progression space for the
Alzheimer's disease into low, moderate and high disease progression zones. The
proposed work will enable early detection and characterization of distinct
disease subtypes based on clinical heterogeneity. We anticipate that our models
will enable patient counseling, clinical trial design, and ultimately
individualized clinical care.
