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Cognitive Development During Adulthood

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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

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Published on: January 11, 2020

Detecting Cognitive Impairment Status Using Keystroke Patterns and Physical Activity Data among the Older Adults: A

Mohammad Nahid Hossain¹, Mohammad Helal Uddin¹, K Thapa¹

¹Department of Electronic Engineering, Kwangwoon University, Seoul 139-701, Republic of Korea.

Journal of Healthcare Engineering

|December 30, 2021

Summary

This summary is machine-generated.

Early detection of cognitive impairment is crucial. This study developed a machine learning model using neurophysical and physical data, achieving over 94% accuracy in classifying cognitive severity levels.

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Area of Science:

Neuroscience
Artificial Intelligence
Gerontology

Background:

Cognitive impairment significantly impacts global healthcare and communities, especially among older adults.
Aging often leads to declines in cognition and mental retention, making early detection vital to prevent permanent mental damage.

Purpose of the Study:

To develop a machine learning model for detecting and differentiating cognitive impairment severity (severe, moderate, mild, normal).
To analyze neurophysical and physical data for improved cognitive impairment prediction.

Main Methods:

Extracted neurophysical data via keystroke dynamics and physical data via smartwatches.
Employed the Gradient Boosting Machine (GBM) ensemble learning algorithm for classification.
Utilized Pearson's correlation and wrapper feature selection for optimal feature identification.

Main Results:

The proposed GBM model achieved an accuracy exceeding 94% in classifying cognitive severity.
Successfully integrated neurophysical and physical data for enhanced prediction capabilities.

Conclusions:

The study demonstrates the efficacy of a machine learning approach combining neurophysical and physical data for accurate cognitive impairment detection.
This research offers a novel dimension to the state-of-the-art in predicting cognitive decline.