Alzheimer’s disease, the most common form of dementia, is an extremely serious health problem, and one that will become even more so in the coming decades as the global population ages. This has led to a massive effort to develop both new treatments for the condition and new methods of diagnosis; in fact the two are intimately linked as future treatments will depend on earlier diagnosis, which in turn requires the development of biomarkers that can be used to identify and track the disease. This is made possible by studies such as the Alzheimer’s disease neuroimaging initiative which provides previously unimaginable quantities of imaging and other data freely to researchers. It is the task of early diagnosis that this thesis focuses on. We do so by borrowing modern machine learning techniques, and applying them to image data. In particular, we use Gaussian processes (GPs), a previously neglected tool, and show they can be used in place of the more widely used support vector machine (SVM). As combinations of complementary biomarkers have been shown to be more useful than the biomarkers are individually, we go on to show GPs can also be applied to integrate different types of image and non-image data, and thanks to their properties this improves results further than it does with SVMs. In the final two chapters, we also look at different ways to formulate both the prediction of conversion to Alzheimer’s disease as a machine learning problem and the way image data can be used to generate features for input as a machine learning algorithm. Both of these show how unconventional approaches may improve results. The result is an advance in the state-of-the-art for a very clinically important problem, which may prove useful in practice and show a direction of future research to further increase the usefulness of such method
