Cluster Analysis and Decision Trees of MR Imaging in Patients Suffering Alzheimer’s

A Hamou; M Bauer; Benoit Lewden; Andrew Simmons; Y Zhang; Lars-Olof Wahlund; Catherine Tunnard; Iwona Kloszewska; Patrizia Mecocci; Hilkka Soininen; Magda Tsolaki; Bruno Vellas; Sebastian Muehlboeck; Anne Evans; Christian Spenger; Simon Lovestone; Femida Gwadry-Sridhar

doi:10.1007/978-3-642-12433-4_56

Cluster Analysis and Decision Trees of MR Imaging in Patients Suffering Alzheimer’s

A Hamou, M Bauer, Benoit Lewden, Andrew Simmons, Y Zhang, Lars-Olof Wahlund, Catherine Tunnard, Iwona Kloszewska, Patrizia Mecocci, Hilkka Soininen, Magda Tsolaki, Bruno Vellas, Sebastian Muehlboeck, Anne Evans, Christian Spenger, Simon Lovestone, Femida Gwadry-Sridhar

Research output: Contribution to journal › Conference paper › peer-review

3 Citations (Scopus)

Abstract

The use of novel analytical techniques (such as data clustering and decision trees) that can model and predict patient disease outcomes has great potential for assessing disease process and progression in Alzheimer’s disease and mild cognitive impairment. For this study, 43 different variables (generated from image data, demographics and clinical data) have been compiled and analyzed using a modified clustering algorithm. Our aim was to determine the influence of these variables on the incidence of Alzheimer’s and mild cognitive impairment. Furthermore, we used a decision tree algorithm to model the level of “importance” of variants influencing this decision.

Original language	English
Pages (from-to)	477-484
Journal	Intelligent systems and soft computing. Prospects, tools and applications (Lecture Notes in Artificial Intelligence)
Volume	71
DOIs	https://doi.org/10.1007/978-3-642-12433-4_56
Publication status	Published - 2010

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Hamou, A., Bauer, M., Lewden, B., Simmons, A., Zhang, Y., Wahlund, L.-O., Tunnard, C., Kloszewska, I., Mecocci, P., Soininen, H., Tsolaki, M., Vellas, B., Muehlboeck, S., Evans, A., Spenger, C., Lovestone, S., & Gwadry-Sridhar, F. (2010). Cluster Analysis and Decision Trees of MR Imaging in Patients Suffering Alzheimer’s. Intelligent systems and soft computing. Prospects, tools and applications (Lecture Notes in Artificial Intelligence), 71, 477-484. https://doi.org/10.1007/978-3-642-12433-4_56

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title = "Cluster Analysis and Decision Trees of MR Imaging in Patients Suffering Alzheimer{\textquoteright}s",

abstract = "The use of novel analytical techniques (such as data clustering and decision trees) that can model and predict patient disease outcomes has great potential for assessing disease process and progression in Alzheimer{\textquoteright}s disease and mild cognitive impairment. For this study, 43 different variables (generated from image data, demographics and clinical data) have been compiled and analyzed using a modified clustering algorithm. Our aim was to determine the influence of these variables on the incidence of Alzheimer{\textquoteright}s and mild cognitive impairment. Furthermore, we used a decision tree algorithm to model the level of “importance” of variants influencing this decision.",

author = "A Hamou and M Bauer and Benoit Lewden and Andrew Simmons and Y Zhang and Lars-Olof Wahlund and Catherine Tunnard and Iwona Kloszewska and Patrizia Mecocci and Hilkka Soininen and Magda Tsolaki and Bruno Vellas and Sebastian Muehlboeck and Anne Evans and Christian Spenger and Simon Lovestone and Femida Gwadry-Sridhar",

year = "2010",

doi = "10.1007/978-3-642-12433-4_56",

language = "English",

volume = "71",

pages = "477--484",

journal = "Intelligent systems and soft computing. Prospects, tools and applications (Lecture Notes in Artificial Intelligence)",

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Hamou, A, Bauer, M, Lewden, B, Simmons, A, Zhang, Y, Wahlund, L-O, Tunnard, C, Kloszewska, I, Mecocci, P, Soininen, H, Tsolaki, M, Vellas, B, Muehlboeck, S, Evans, A, Spenger, C, Lovestone, S & Gwadry-Sridhar, F 2010, 'Cluster Analysis and Decision Trees of MR Imaging in Patients Suffering Alzheimer’s', Intelligent systems and soft computing. Prospects, tools and applications (Lecture Notes in Artificial Intelligence), vol. 71, pp. 477-484. https://doi.org/10.1007/978-3-642-12433-4_56

TY - JOUR

T1 - Cluster Analysis and Decision Trees of MR Imaging in Patients Suffering Alzheimer’s

AU - Hamou, A

AU - Bauer, M

AU - Lewden, Benoit

AU - Simmons, Andrew

AU - Zhang, Y

AU - Wahlund, Lars-Olof

AU - Tunnard, Catherine

AU - Kloszewska, Iwona

AU - Mecocci, Patrizia

AU - Soininen, Hilkka

AU - Tsolaki, Magda

AU - Vellas, Bruno

AU - Muehlboeck, Sebastian

AU - Evans, Anne

AU - Spenger, Christian

AU - Lovestone, Simon

AU - Gwadry-Sridhar, Femida

PY - 2010

Y1 - 2010

N2 - The use of novel analytical techniques (such as data clustering and decision trees) that can model and predict patient disease outcomes has great potential for assessing disease process and progression in Alzheimer’s disease and mild cognitive impairment. For this study, 43 different variables (generated from image data, demographics and clinical data) have been compiled and analyzed using a modified clustering algorithm. Our aim was to determine the influence of these variables on the incidence of Alzheimer’s and mild cognitive impairment. Furthermore, we used a decision tree algorithm to model the level of “importance” of variants influencing this decision.

AB - The use of novel analytical techniques (such as data clustering and decision trees) that can model and predict patient disease outcomes has great potential for assessing disease process and progression in Alzheimer’s disease and mild cognitive impairment. For this study, 43 different variables (generated from image data, demographics and clinical data) have been compiled and analyzed using a modified clustering algorithm. Our aim was to determine the influence of these variables on the incidence of Alzheimer’s and mild cognitive impairment. Furthermore, we used a decision tree algorithm to model the level of “importance” of variants influencing this decision.

U2 - 10.1007/978-3-642-12433-4_56

DO - 10.1007/978-3-642-12433-4_56

M3 - Conference paper

VL - 71

SP - 477

EP - 484

JO - Intelligent systems and soft computing. Prospects, tools and applications (Lecture Notes in Artificial Intelligence)

JF - Intelligent systems and soft computing. Prospects, tools and applications (Lecture Notes in Artificial Intelligence)

ER -

Cluster Analysis and Decision Trees of MR Imaging in Patients Suffering Alzheimer’s

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