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Updated: Jul 2, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Use of the binomial distribution to predict impairment: application in a nonclinical sample
Bradley N Axelrod1, Jacqueline R Wall, Bradley W Estes
1John D. Dingell Department of Veterans Affairs, Detroit, Michigan, USA.
Abstract:
A mathematical model based on the binomial theory was developed to illustrate when abnormal score variations occur by chance in a multitest battery (Ingraham & Aiken, 1996). It has been successfully used as a comparison for obtained test scores in clinical samples, but not in nonclinical samples. In the current study, this model has been applied to demographically corrected scores on the Halstead-Reitan Neuropsychological Test Battery, obtained from a sample of 94 nonclinical college students. Results found that 15% of the sample had impairments suggested by the Halstead Impairment Index, using criteria established by Reitan and Wolfson (1993). In addition, one-half of the sample obtained impaired scores on one or two tests. These results were compared to that predicted by the binomial model and found to be consistent. The model therefore serves as a useful resource for clinicians considering the probability of impaired test performance.
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