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

Giuletti, G; Dayan, M; Serra, L; Tuzzi, E; Spano, B; Cercignani, M; Caltagirone, C; Bozzali, M

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

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http://archive.ismrm.org/2013/1000.html
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Giuletti, G., Dayan, M., Serra, L., Tuzzi, E., Spano, B., Cercignani, M., et al. (2013). Alzheimer's Disease Prediction Based on Machine Learning Methods Applied to Multimodal MR Features. Poster presented at 21st Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM 2013), Salt Lake City, UT, USA.

Cite as: https://hdl.handle.net/21.11116/0000-0001-55AF-B

Abstract

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.