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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
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Prediction of Mild Cognitive Impairment Using Movement Complexity
IEEE Journal of Biomedical and Health Informatics
|April 15, 2020
Summary
Movement analysis using machine learning can predict mild cognitive impairment (MCI) onset and progression up to six months earlier than clinical diagnosis. This technology aids in monitoring cognitive behavior in independent living settings.
Area of Science:
- Neurology
- Computer Science
- Gerontology
Background:
- Aimless movement and wandering are potential early indicators of mild cognitive impairment (MCI), often linked to confusion and forgetfulness.
- Independent living environments can be monitored using smart home technology to track behavioral changes.
Purpose of the Study:
- To develop and validate a machine learning framework for predicting the onset and progression of MCI using movement analysis.
- To assess the efficacy of Support Vector Machine (SVM) algorithms in identifying MCI-related behavioral changes.
Main Methods:
- Collected ten-year movement data from 44 subjects (22 MCI, 22 healthy) in smart homes using motion sensors.
- Extracted movement features including cyclomatic complexity, fractal index, entropy, and room transitions.
- Trained two SVM classification algorithms to predict MCI onset and progression.
Main Results:
- The SVM models detected MCI onset six months earlier than traditional clinical diagnosis.
- Model accuracy for MCI classification improved over time, reaching 81% by the 11th post-transition month.
- Cyclomatic complexity emerged as a significant predictor of MCI onset and progression.
Conclusions:
- Movement complexity metrics and machine learning are effective tools for monitoring cognitive behavior in individuals living independently.
- This approach offers potential for earlier detection and management of MCI.
