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  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., 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.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0001-55AF-B Version Permalink: http://hdl.handle.net/21.11116/0000-0001-55B0-8
Genre: Poster

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 Creators:
Giuletti, G, Author
Dayan, M, Author
Serra, L, Author
Tuzzi, E1, Author              
Spano, B, Author
Cercignani, M, Author
Caltagirone, C, Author
Bozzali, M, Author
Affiliations:
1Neuroimaging Laboratory, Santa Lucia Foundation, Rome, Italy, ou_persistent22              

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 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.

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 Dates: 2013-04
 Publication Status: Published in print
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Title: 21st Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM 2013)
Place of Event: Salt Lake City, UT, USA
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Title: 21st Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM 2013)
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: 1000 Start / End Page: - Identifier: -