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Cognitive Development During Adulthood

Cognitive Development During Adulthood

Cognitive development continues throughout adulthood, undergoing significant shifts across early, middle, and late stages. Individual transition occurs from adolescent idealism to pragmatic and adaptable thinking in early adulthood. During this period, individuals learn to integrate personal beliefs with the recognition that other perspectives are equally valid. Exposure to the complexities of modern society, diverse experiences, and higher education contribute to this adaptive thought process,...

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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

Identifying Key Predictors of Cognitive Dysfunction in Older People Using Supervised Machine Learning Techniques:

Debbie Rankin¹, Michaela Black¹, Bronac Flanagan¹

¹School of Computing, Engineering and Intelligent Systems, Ulster University, Derry~Londonderry, United Kingdom.

JMIR Medical Informatics

|September 16, 2020

Summary

Machine learning models identified key predictors of poorer cognitive performance in older adults. Simple, non-invasive questions can help healthcare professionals quickly assess dementia risk, saving time and resources.

Keywords:

aging classification cognition diet geriatric assessment supervised machine learning

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Area of Science:

Gerontology
Neuroscience
Computational Biology

Background:

Cognitive decline is a significant concern in aging populations.
Early identification of risk factors for cognitive impairment is crucial for timely intervention.
Machine learning offers potential for analyzing complex health data to predict cognitive function.

Purpose of the Study:

To employ classification techniques to pinpoint key patient predictors of poorer cognitive performance.
To identify critical factors associated with an increased risk of dementia.
To analyze health, nutritional, and environmental predictors of cognitive function and decline.

Main Methods:

Utilized data from the Trinity-Ulster and Department of Agriculture study (n=5186 older adults).
Employed decision trees, Naïve Bayes, and random forests classifiers to analyze cognitive function (Repeatable Battery for the Assessment of Neuropsychological Status - RBANS).
Identified key predictors for classifying low RBANS scores and cognitive decline over a 5-7 year follow-up period.

Main Results:

Classification models achieved high performance (F1 score 0.73-0.93 for low RBANS; 0.66-0.85 for decline).
Key predictors for poorer cognitive performance included Timed Up and Go (TUG) test scores, age of education cessation, and reported family memory concerns.
Predictors for cognitive decline included TUG scores, plasma homocysteine, vitamin B6, and glycated hemoglobin.

Conclusions:

A few non-invasive questions, including TUG scores and reported memory concerns, can effectively predict cognitive dysfunction.
This approach allows for rapid, cost-effective initial cognitive risk assessments by healthcare professionals.
Potential to reduce unnecessary cognitive evaluations and associated patient stress.