Machine-learning techniques for building a diagnostic model for very mild dementia.
Many researchers have sought to construct diagnostic models to differentiate individuals with very mild dementia (VMD) from healthy elderly people, based on structural magnetic-resonance (MR) images. These models have, for the most part, been based on discriminant analysis or logistic regression, with few reports of alternative approaches. To determine the relative strengths of different approaches to analyzing structural MR data to distinguish people with VMD from normal elderly control subjects, we evaluated seven different classification approaches, each of which we used to generate a diagnostic model from a training data set acquired from 83 subjects (33 VMD and 50 control). We then evaluated each diagnostic model using an independent data set acquired from 30 subjects (13 VMD and 17 controls). We found that there were significant performance differences across these seven diagnostic models. Relative to the diagnostic models generated by discriminant analysis and logistic regression, the diagnostic models generated by other high-performance diagnostic-model-generation algorithms manifested increased generalizability when diagnostic models were generated from all atlas structures.
Chen
Rong
R
Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA. rong.chen@uphs.upenn.edu
Herskovits
Edward H
EH
D000368:Aged; D000465:Algorithms; D001185:Artificial Intelligence; D001271:Atlases as Topic; D001921:Brain / Q000473:pathology*; D019992:Databases as Topic; D003704:Dementia / Q000175:diagnosis* / Q000473:pathology*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D016002:Discriminant Analysis; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D016015:Logistic Models; D008279:Magnetic Resonance Imaging; D008297:Male; D008959:Models, Neurological; D012680:Sensitivity and Specificity; D013702:Temporal Lobe / Q000473:pathology
Neuroimage. 2008 Jul 15;41(4):1220-7
18474436
Ann Neurol. 2000 Apr;47(4):430-9
10762153
Acta Neuropathol. 1991;82(4):239-59
1759558
Arch Neurol. 2000 May;57(5):675-80
10815133
Arch Neurol. 2005 Dec;62(12):1870-6
16344345
Neuroimaging Clin N Am. 2005 Nov;15(4):869-77, xi-xii
16443497
Hippocampus. 2009 Feb;19(2):166-75
18777563
Arch Neurol. 2001 Mar;58(3):397-405
11255443
J Am Geriatr Soc. 1997 Mar;45(3):321-8
9063278
Neurology. 1993 Nov;43(11):2412-4
8232972
Cereb Cortex. 2005 Jun;15(6):732-9
15371293
IEEE Trans Med Imaging. 2002 Nov;21(11):1421-39
12575879
Neurobiol Aging. 2001 Mar-Apr;22(2):177-86
11182467
Lancet. 2006 Apr 15;367(9518):1262-70
16631882
Neurobiol Aging. 2001 Jul-Aug;22(4):529-39
11445252
Neurobiol Aging. 2008 Apr;29(4):514-23
17174012
Neurobiol Aging. 2004 Aug;25(7):913-24
15212845
Neurobiol Aging. 1997 Mar-Apr;18(2):131-8
9258889
Neurobiol Aging. 2004 Mar;25(3):303-10
15123335
Brain. 2008 Mar;131(Pt 3):681-9
18202106
J Comput Assist Tomogr. 1998 Sep-Oct;22(5):827-37
9754125
J Neurol Neurosurg Psychiatry. 2001 Oct;71(4):441-7
11561025
Neuroimage. 2007 Jan 1;34(1):235-42
17045492
J Cogn Neurosci. 2007 Sep;19(9):1498-507
17714011
Neuroimage. 2006 Feb 15;29(4):1252-9
16213161