NDLI: Fall risk classification with posturographic parameters in community-dwelling older adults: a machine learning and explainable artificial intelligence approach

Content Provider	Springer Nature : BioMed Central
Author	Liang, Huey-Wen Ameri, Rasoul Band, Shahab Chen, Hsin-Shui Ho, Sung-Yu Zaidan, Bilal Chang, Kai-Chieh Chang, Arthur
Abstract	Background Computerized posturography obtained in standing conditions has been applied to classify fall risk for older adults or disease groups. Combining machine learning (ML) approaches is superior to traditional regression analysis for its ability to handle complex data regarding its characteristics of being high-dimensional, non-linear, and highly correlated. The study goal was to use ML algorithms to classify fall risks in community-dwelling older adults with the aid of an explainable artificial intelligence (XAI) approach to increase interpretability. Methods A total of 215 participants were included for analysis. The input information included personal metrics and posturographic parameters obtained from a tracker-based posturography of four standing postures. Two classification criteria were used: with a previous history of falls and the timed-up-and-go (TUG) test. We used three meta-heuristic methods for feature selection to handle the large numbers of parameters and improve efficacy, and the SHapley Additive exPlanations (SHAP) method was used to display the weights of the selected features on the model. Results The results showed that posturographic parameters could classify the participants with TUG scores higher or lower than 10 s but were less effective in classifying fall risk according to previous fall history. Feature selections improved the accuracy with the TUG as the classification label, and the Slime Mould Algorithm had the best performance (accuracy: 0.72 to 0.77, area under the curve: 0.80 to 0.90). In contrast, feature selection did not improve the model performance significantly with the previous fall history as a classification label. The SHAP values also helped to display the importance of different features in the model. Conclusion Posturographic parameters in standing can be used to classify fall risks with high accuracy based on the TUG scores in community-dwelling older adults. Using feature selection improves the model’s performance. The results highlight the potential utility of ML algorithms and XAI to provide guidance for developing more robust and accurate fall classification models. Trial registration Not applicable
Related Links	https://jneuroengrehab.biomedcentral.com/counter/pdf/10.1186/s12984-024-01310-3.pdf
Ending Page	17
Page Count	17
Starting Page	1
File Format	HTM / HTML
ISSN	17430003
DOI	10.1186/s12984-024-01310-3
Journal	Journal of NeuroEngineering and Rehabilitation
Issue Number	1
Volume Number	21
Language	English
Publisher	BioMed Central
Publisher Date	2024-01-29
Access Restriction	Open
Subject Keyword	Neurosciences Neurology Rehabilitation Medicine Biomedical Engineering and Bioengineering Falls Machine learning Older adults Risk classification Trunk sway
Content Type	Text
Resource Type	Article
Subject	Health Informatics Rehabilitation
Journal Impact Factor	5.2/2023
5-Year Journal Impact Factor	5.6/2023

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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