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Title:

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

Document type:
Article; Journal Article
Author(s):
Lampe, Leonie; Niehaus, Sebastian; Huppertz, Hans-Jürgen; Merola, Alberto; Reinelt, Janis; Mueller, Karsten; Anderl-Straub, Sarah; Fassbender, Klaus; Fliessbach, Klaus; Jahn, Holger; Kornhuber, Johannes; Lauer, Martin; Prudlo, Johannes; Schneider, Anja; Synofzik, Matthis; Danek, Adrian; Diehl-Schmid, Janine; Otto, Markus; Villringer, Arno; Egger, Karl; Hattingen, Elke; Hilker-Roggendorf, Rüdiger; Schnitzler, Alfons; Südmeyer, Martin; Oertel, Wolfgang; Kassubek, Jan; Höglinger, Günter; Schroeter,...     »
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...     »
Journal title abbreviation:
Alzheimers Res Ther
Year:
2022
Journal volume:
14
Journal issue:
1
Fulltext / DOI:
doi:10.1186/s13195-022-00983-z
Pubmed ID:
http://view.ncbi.nlm.nih.gov/pubmed/35505442
TUM Institution:
1367; 303; 55; 617; 702; Klinik und Poliklinik für Psychiatrie und Psychotherapie
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