Integrating Different Data Modalities for the Classification of Alzheimer's Disease Stages

Alzheimer's disease (AD) is the most common form of dementia with physical, psychological, social, and economic impacts on patients, their carers, and society. Its early diagnosis allows clinicians to initiate the treatment as early as possible to arrest or slow down the disease progression more effectively. We consider the problem of classifying AD patients through a machine learning approach using different data modalities acquired by non-invasive techniques. We perform an extensive evaluation of a machine learning classification procedure using omics, imaging, and clinical features, extracted by the ANMerge dataset, taken alone or combined together. Experimental results suggest that integrating omics and imaging features leads to better performance than any of them taken separately. Moreover, clinical features consisting of just two cognitive test scores always lead to better performance than any of the other types of data or their combinations. Since these features are usually involved in the clinician diagnosis process, our results show how their adoption as classification features positively biases the results.