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Updated: May 25, 2026
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
A method for selecting an efficient diagnostic protocol for classification of perceptive and cognitive impairments in
Kunjan D Rana1, Benvy Caldwell, Lucia M Vaina
1Brain and Vision Research Laboratory, Department of Biomedical Engineering, Boston University, Boston, MA, USA. kdrana@bu.edu
Abstract:
An important and unresolved problem in the assessment of perceptual and cognitive deficits in neurological patients is how to choose from the many existing behavioral tests, a subset that is sufficient for an appropriate diagnosis. This problem has to be dealt with in clinical trials, as well as in rehabilitation settings and often even at bedside in acute care hospitals. The need for efficient, cost effective and accurate diagnostic-evaluations, in the context of clinician time constraints and concerns for patients' fatigue in long testing sessions, make it imperative to select a set of tests that will provide the best classification of the patient's deficits. However, the small sample size of the patient population complicates the selection methodology and the potential accuracy of the classifier. We propose a method that allows for ordering tests based on having progressive increases in classification using cross-validation to assess the classification power of the chosen test set. This method applies forward linear regression to find an ordering of the tests with leave-one-out cross-validation to quantify, without biasing to the training set, the classification power of the chosen tests.
