Classification and staging of dementia of the Alzheimer type: a comparison between neural networks and linear discriminant analysis
B. M. French, M. R. Dawson and A. R. Dobbs
Department of Psychology, University of Alberta, Edmonton, Canada. bfrench@psych.ualberta.ca
OBJECTIVE: To examine the utility of artificial neural networks (ANNs) for
differentiating patients with Alzheimer disease from healthy control
subjects and for staging the degree of dementia. DESIGN: Comparison of the
classification abilities of ANNs with the statistical technique of linear
discriminant analysis (LDA) using the results of 11 neuropsychological
tests as predictors. PARTICIPANTS: Ninety-two patients with a diagnosis of
probable Alzheimer disease (referred from a geriatric clinic) and 43
elderly control subjects (independently solicited). The patients met
National Institute of Neurological and Communicative Disorders and
Stroke-Alzheimer's Disease and Related Disorders Association criteria for
probable dementia, with clinical ratings of dementia severity derived from
the Cambridge Examination for Mental Disorders of the Elderly (CAMDEX).
MAIN OUTCOME MEASURES: Classifications between and within groups were
determined by using LDA and ANNs, and more detailed comparisons of the 2
methods were performed by using chi2 analyses and unweighted and weighted
kappa statistics. RESULTS: Linear discriminant analysis correctly
identified 71.9% of cases. Artificial neural networks, trained to classify
the subjects using the same data, correctly classified 91.1% of the cases.
Subsidiary analyses showed that although both techniques effectively
discriminated between the control subjects and patients with dementia, the
ANNs were more powerful in discriminating severity levels within the
dementia population. The analyses for goodness of fit revealed that the ANN
classification produced a better fit to the actual data. A comparison of
the weighted proportion of agreement between the criterion and predictor
variables also showed that the ANNs clearly outperformed LDA in
classification accuracy for the full data set and patients-only data set.
CONCLUSION: The results demonstrate the utility of ANNs for group
classification of patients with Alzheimer disease and elderly controls and
for staging dementia severity using neuropsychological data.
