Comparative analysis of machine learning algorithms for multi-syndrome classification of neurodegenerative syndromes.
Lampe L, Niehaus S, Huppertz HJ, Merola A, Reinelt J, Mueller K, Anderl-Straub S, Fassbender K, Fliessbach K, Jahn H, Kornhuber J, Lauer M, Prudlo J, Schneider A, Synofzik M, Danek A, Diehl-Schmid J, Otto M; FTLD-Consortium Germany; Villringer A, Egger K, Hattingen E, Hilker-Roggendorf R, Schnitzler A, Südmeyer M, Oertel W; German Atypical Parkinson Consortium Study Group; Kassubek J, Höglinger G, Schroeter ML.
Lampe L, et al.
Alzheimers Res Ther. 2022 May 3;14(1):62. doi: 10.1186/s13195-022-00983-z.
Alzheimers Res Ther. 2022.
PMID: 35505442
Free PMC article.
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 clas …
RESULTS: Overall, the neural network produced both the best performance measures and the most robust results. The smaller classes however we …