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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Neuropsychological test selection for cognitive impairment classification: A machine learning approach.

Alyssa Weakley¹, Jennifer A Williams, Maureen Schmitter-Edgecombe

¹a Department of Psychology , Washington State University , Pullman , WA , USA.

Journal of Clinical and Experimental Neuropsychology

|September 3, 2015

Summary

Machine learning accurately classifies cognitive impairment using fewer tests. This research identifies the minimal clinical measures needed for diagnosing healthy older adults, mild cognitive impairment (MCI), and dementia.

Keywords:

Dementia Diagnosis Machine learning Mild cognitive impairment Naive Bayes

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Area of Science:

Computational neuroscience
Geriatric medicine
Machine learning applications in healthcare

Background:

Accurate and efficient detection of cognitive impairment is crucial for clinical practice.
Current diagnostic processes may involve extensive testing, highlighting the need for streamlined approaches.

Purpose of the Study:

To determine the minimum number of clinical measures required for accurate classification of cognitive status.
To compare the efficacy of machine learning techniques in classifying individuals as healthy, mild cognitive impairment (MCI), or dementia.

Main Methods:

Explored machine learning models (naive Bayes, decision tree) and logistic regression.
Utilized two datasets: clinical diagnosis and Clinical Dementia Rating (CDR) scores.
Analyzed 27 demographic, psychological, and neuropsychological variables for selection.

Main Results:

Machine learning models demonstrated satisfactory classification performance (70.0-99.1%) across datasets.
Variable selection identified that only 2-9 variables were necessary for accurate classification.
Mild cognitive impairment (MCI) and CDR=0.5 groups presented the greatest classification challenge.

Conclusions:

Machine learning techniques effectively classify cognitive impairment.
The number of required diagnostic measures can be significantly reduced using these methods.