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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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Using machine learning to modify and enhance the daily living questionnaire.
Peleg Panovka1, Yaron Salman1, Hagit Hel-Or1
1Department of Computer Science, University of Haifa, Haifa, Israel.
Digital Health
|May 1, 2023
Summary
Machine learning effectively shortened the Daily Living Questionnaire (DLQ) by over 50% while maintaining 95% accuracy. This abbreviated DLQ enables wider screening and improves clinical utility.
Area of Science:
- Cognitive assessment
- Machine learning applications
- Psychometrics
Background:
- The Daily Living Questionnaire (DLQ) is a functional cognitive measure used in medical and rehabilitation settings.
- Its length presents challenges for efficient public screening, leading to potential inaccuracies due to subject fatigue.
Purpose of the Study:
- To utilize Machine Learning (ML) to shorten the DLQ without sacrificing accuracy or fidelity.
- To develop a more efficient and user-friendly version of the DLQ for broader application.
Main Methods:
- An ML-based Computerized Adaptive Testing (ML-CAT) algorithm was applied to DLQ data from studies in the USA and Israel.
- The ML-CAT algorithm created an adaptive testing instrument with a shortened form tailored to individual scores.
Main Results:
- The ML-CAT approach reduced the number of required tests by 25% for individual DLQ scores and over 50% when predicting all seven scores concurrently.
- Accuracy was maintained at 95% (5% error) across all subject scores.
- The study identified specific DLQ items most predictive of overall scores.
Conclusions:
- The ML-CAT model offers a method to modify, refine, and abridge the DLQ.
- This abbreviated DLQ can facilitate wider community screening and enhance clinical and research utility.
