Abstract #1000

Alzheimer's Disease Prediction Based on Machine Learning Methods Applied to Multimodal MR Features

Giovanni Giulietti¹, Michael Dayan¹, Laura Serra¹, Elisa Tuzzi¹, Barbara Spano'¹, Mara Cercignani², Carlo Caltagirone³,⁴, Marco Bozzali¹

¹Neuroimaging Laboratory, Santa Lucia Foundation, Rome, Italy; ²Clinical Imaging Sciences Centre, Brighton & Sussex Medical School, Brighton, United Kingdom; ³Clinical and Behavioural Neurology, IRCCS Santa Lucia Foundation, Rome, Italy; ⁴Departement of Neuroscience, "Tor Vergata" University, Rome, Italy

In the current study, we investigated the classification between healthy subjects and patients with Alzheimers disease, using structural (T1) and diffusion (DWI) MR data as input to Support Vector Machine (SVM) classifiers. SVM based on T1 features had higher discrimination capability relative to SVM based on DWI, but the best classification performance (92.6% of accuracy) was obtained combining them. We achieved satisfactory result despite the utilization of a small number of features, considering that it is not uncommon to use hundreds features to improve the classification performance. This evidence make our approach suitable to be adopted into clinical practice.