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  Comparative analysis of machine learning algorithms for multi-syndrome classification of neurodegenerative syndromes

Lampe, L., Niehaus, S., Huppertz, H.-J., Merola, A., Reinelt, J., Mueller, K., et al. (2022). Comparative analysis of machine learning algorithms for multi-syndrome classification of neurodegenerative syndromes. Alzheimer's Research & Therapy, 14: 62. doi:10.1186/s13195-022-00983-z.

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 Creators:
Lampe, Leonie1, Author              
Niehaus, Sebastian2, Author
Huppertz, Hans-Jürgen2, Author
Merola, Alberto2, Author
Reinelt, Janis2, Author
Mueller, Karsten2, Author
Anderl-Straub, Sarah2, Author
Fassbender, Klaus2, Author
Fliessbach, Klaus2, Author
Jahn, Holger2, Author
Kornhuber, Johannes2, Author
Lauer, Martin2, Author
Prudlo, Johannes2, Author
Schneider, Anja2, Author
Synofzik, Matthis2, Author
Danek, Adrian2, Author
Diehl-Schmid, Janine2, Author
Otto, Markus2, Author
Villringer, Arno1, Author              
Egger, Karl2, Author
Hattingen, Elke2, AuthorHilker-Roggendorf, Rüdiger2, AuthorSchnitzler, Alfons2, AuthorSüdmeyer, Martin2, AuthorOertel, Wolfgang2, AuthorKassubek, Jan2, AuthorHöglinger, Günter2, AuthorSchroeter, Matthias L.1, Author               more..
Affiliations:
1Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
2External Organizations, ou_persistent22              

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Free keywords: Comparative analysis; Deep neural network; Gradient boosting; Multi-syndrome classification; Neurodegenerative syndromes; Random forest; Support vector machine
 Abstract: Importance: The entry of artificial intelligence into medicine is pending. Several methods have been used for the predictions of structured neuroimaging data, yet nobody compared them in this context. Objective: Multi-class prediction is key for building computational aid systems for differential diagnosis. We compared support vector machine, random forest, gradient boosting, and deep feed-forward neural networks for the classification of different neurodegenerative syndromes based on structural magnetic resonance imaging. Design, setting, and participants: Atlas-based volumetry was performed on multi-centric T1-weighted MRI data from 940 subjects, i.e., 124 healthy controls and 816 patients with ten different neurodegenerative diseases, leading to a multi-diagnostic multi-class classification task with eleven different classes. Interventions: N.A. Main outcomes and measures: Cohen's kappa, accuracy, and F1-score to assess model performance. Results: Overall, the neural network produced both the best performance measures and the most robust results. The smaller classes however were better classified by either the ensemble learning methods or the support vector machine, while performance measures for small classes were comparatively low, as expected. Diseases with regionally specific and pronounced atrophy patterns were generally better classified than diseases with widespread and rather weak atrophy. Conclusions and relevance: Our study furthermore underlines the necessity of larger data sets but also calls for a careful consideration of different machine learning methods that can handle the type of data and the classification task best.

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Language(s): eng - English
 Dates: 2021-01-282022-02-242022-05-03
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1186/s13195-022-00983-z
PMID: 35505442
PMC: PMC9066923
 Degree: -

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Project name : -
Grant ID : FKZ O1GI1007A
Funding program : -
Funding organization : German Federal Ministry of Education and Research (BMBF)
Project name : -
Grant ID : SCHR 774/5-1
Funding program : -
Funding organization : German Research Foundation (DFG)
Project name : -
Grant ID : PDF-IRG-1307
Funding program : -
Funding organization : Parkinson’s Disease Foundation
Project name : -
Grant ID : MJFF-11362
Funding program : -
Funding organization : Michael J. Fox Foundation

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Title: Alzheimer's Research & Therapy
Source Genre: Journal
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Affiliations:
Publ. Info: BioMed Central
Pages: - Volume / Issue: 14 Sequence Number: 62 Start / End Page: - Identifier: ISSN: 1758-9193
CoNE: https://pure.mpg.de/cone/journals/resource/1758-9193