Machine learning for very early Alzheimer's Disease diagnosis; a <sup>18</sup>F-FDG and PiB PET comparison

Abstract

This paper shows a machine learning approach based on Principal Component Analysis (PCA) and Support Vector Machine (SVM) to compare the diagnostic accuracy on very early Alzheimer's Disease (AD) patients with <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">18</sup> F FDG and Pittsburg Compound B (PiB) PET imaging. The Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset is used for testing, making use of the longitudinal character. Mild Cognitive Impairment (MCI) individuals that after a two years follow up converted into possible AD where used as very early AD patients. While <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">18</sup> F FDG and PiB have similar diagnostic accuracy in AD, PiB is shown to have higher discriminative power in very early AD with respect to FDG.