An Oblique Approach to Prediction of Conversion to Alzheimer’s Disease with Multikernel Gaussian Processes

Abstract

Machine learning approaches have had some success in predicting conversion to Alzheimer’s Disease (AD) in subjects with mild cognitive impairment (MCI), a less serious condition that nonetheless is a risk factor for AD. Predicting conversion is clinically important as because novel drugs currently being developed require administration early in the disease process to be effective. Traditionally training data are labelled with discrete disease states; which may explain the limited accuracies obtained as labels are noisy due to the difficulty in providing a definitive diagnosis of Alzheimer’s without post-mortem confirmation, and ignore the existence of a continuous spectrum of disease severity. Here, we dispense with discrete training labels and instead predict the loss of brain volume over one year, a quantity that can be repeatably and objectively measured with the boundary shift integral and is strongly correlated with conversion. The method combines MRI and PET image data and cerebrospinal fluid biomarker levels in an Bayesian multi-kernel learning framework. The resulting predicted atrophy separates converting and non-converting MCI subjects with 74.6 % accuracy, which compares well to state of the art methods despite a small training set size.