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Updated: May 29, 2025
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Detecting mild cognitive impairment by applying integrated random forest to finger tapping
Yuko Sano1, Shota Suzumura2,3, Junpei Sugioka2
1Center for Digital Services, Healthcare Innovation, Research and Development Group, Hitachi, Ltd., Kokubunji, Japan. yuko.sano.hd@hitachi.com.
Abstract:
Early detection of dementia is essential to reduce the decline in quality of life (QoL) and the increase in medical and nursing care costs associated with dementia in an aging society. In this study, we aimed to develop a simple screening test for mild cognitive impairment (MCI), a preliminary stage of dementia, by creating an analytical method to accurately detect MCI through finger-tapping measurement. We extracted 248 characteristics from the finger-tapping waveforms of 182 MCI patients and 352 normal controls, applying five conventional classification methods along with an improved Random Forest (RF) method proposed in this study (Integrated RF). In the proposed method, the RF classification model for the MCI and normal control groups is supplementally integrated with the RF classification model for the Alzheimer's disease and normal control groups to generate a new classification model. When comparing the discrimination accuracy of each method, the proposed method achieved the highest accuracy, with an F1-score of 0.795 (recall = 0.778 and precision = 0.814). These results demonstrate the potential of finger-tapping measurement as a highly accurate screening test for MCI.
Insights
Early dementia detection is crucial. Finger-tapping analysis offers a simple, accurate screening method for mild cognitive impairment (MCI), a dementia precursor, improving quality of life and reducing care costs.
Area of Science:
- Neurology
- Biomedical Engineering
- Gerontology
Background:
- Early detection of dementia is vital for maintaining quality of life and managing healthcare costs in aging populations.
- Mild cognitive impairment (MCI) is a precursor to dementia, necessitating effective screening tools.
- Current screening methods may lack simplicity or accuracy for widespread use.
Purpose of the Study:
- To develop a simple and accurate screening test for mild cognitive impairment (MCI).
- To utilize finger-tapping measurement as a novel diagnostic tool for MCI.
- To enhance classification accuracy for MCI detection using an improved machine learning approach.
Main Methods:
- Extracted 248 features from finger-tapping waveforms of 182 MCI patients and 352 healthy controls.
- Applied five conventional classification methods and an improved Random Forest (RF) method (Integrated RF).
- The Integrated RF model combined MCI vs. normal and Alzheimer's disease vs. normal classification models.
Main Results:
- The proposed Integrated RF method achieved the highest discrimination accuracy.
- Achieved an F1-score of 0.795, with a recall of 0.778 and precision of 0.814.
- Demonstrated superior performance compared to conventional classification methods.
Conclusions:
- Finger-tapping measurement shows significant potential as a highly accurate screening tool for MCI.
- This method offers a simple, non-invasive approach for early detection of cognitive decline.
- Further validation could lead to widespread clinical application for dementia prevention strategies.
