Predicting Falls from Operational Data: Insights and Limitations of Using a Non-Specialized Database

Julien Räker, Patrick Elfert, Cletus Brauer, Marco Eichelberg, Frerk Müller-von Aschwege, Andreas Hein

2025

Abstract

Falls among the elderly are a significant public health concern. This study investigates the feasibility of predicting falls using an operational dataset from Johanniter-Unfall-Hilfe (JUH) home emergency call system, which was not created under laboratory conditions for scientific purposes. An anonymized dataset containing records from 160,281 participants in Germany was analyzed. Statistical analysis identified 104 out of 400 features significantly associated with falls, though with weak correlations (Cramer’s V ranging from 0.006 to 0.071). A one-class Support Vector Machine (SVM) was employed due to the absence of explicit non-fall cases, achieving a true positive rate of 55.10%. The lack of explicit non-fall data prevented evaluation of specificity and overall accuracy. The study demonstrates the potential of using operational datasets for fall prediction but highlights significant limitations due to data quality issues, such as the lack of explicit fall records, absence of non-fall cases, lack of temporal data, and missing values. Recommendations are made to improve data collection practices to enhance the utility of such datasets for predictive modeling.

Paper Citation

in Harvard Style

Räker J., Elfert P., Brauer C., Eichelberg M., Müller-von Aschwege F. and Hein A. (2025). Predicting Falls from Operational Data: Insights and Limitations of Using a Non-Specialized Database. In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF; ISBN 978-989-758-731-3, SciTePress, pages 774-780. DOI: 10.5220/0013298600003911

in Bibtex Style

@conference{healthinf25,
author={Julien Räker and Patrick Elfert and Cletus Brauer and Marco Eichelberg and Frerk Müller-von Aschwege and Andreas Hein},
title={Predicting Falls from Operational Data: Insights and Limitations of Using a Non-Specialized Database},
booktitle={Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF},
year={2025},
pages={774-780},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013298600003911},
isbn={978-989-758-731-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF
TI - Predicting Falls from Operational Data: Insights and Limitations of Using a Non-Specialized Database
SN - 978-989-758-731-3
AU - Räker J.
AU - Elfert P.
AU - Brauer C.
AU - Eichelberg M.
AU - Müller-von Aschwege F.
AU - Hein A.
PY - 2025
SP - 774
EP - 780
DO - 10.5220/0013298600003911
PB - SciTePress