TurC. Machine and deep learning in MS research are just powerful statistics—Yes. Mult Scler2021; 27(5): 661–662.
2.
HealyBC. Machine and deep learning in MS research are just powerful statistics—No. Mult Scler2021; 27(5): 663–664.
3.
BreimanLFriedmanJHOlshenRA, et al. Classification and regression trees. Belmont, CA: Wadsworth, 1984.
4.
BreimanL.Random forest. Mach Learn2001; 45: 5–32.
5.
VapnikV.Statistical learning theory. New York: John Wiley & Sons, 1998.
6.
FriedmanJH.Greedy function approximation: A gradient boosting machine. Ann Statist2001; 29(5): 1189–1232.
7.
GoodfellowIBengioYCourvilleA.Deep learning. Cambridge, MA: MIT Press, 2016.
8.
HaykinS.Neural network. 2nd ed.Upper Saddle River, NJ: Pearson Education, 2009.
9.
KacarKRoccaMACopettiM, et al. Overcoming the clinical–MR imaging paradox of multiple sclerosis: MR imaging data assessed with a random forest approach. AJNR Am J Neuroradiol2011; 32(11): 2098–2102.
10.
BuyukturkogluKZengDBharadwajS, et al. Classifying multiple sclerosis patients on the basis of SDMT performance using machine learning. Mult Scler2021; 27(1): 107–116.