Authors:
Julien Räker
1
;
Patrick Elfert
1
;
Cletus Brauer
2
;
Marco Eichelberg
1
;
Frerk Müller-von Aschwege
1
and
Andreas Hein
1
Affiliations:
1
R&D Division Health, OFFIS - Institute for Information Technology, Oldenburg, Germany
;
2
Johanniter-Unfall-Hilfe e.V., Oldenburg, Germany
Keyword(s):
Fall Prediction, Machine Learning, Health Data, Elderly Care, Predictive Analytics.
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-f
all 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.
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