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Hassler, AP; Menasalvas, E; Garcia-Garcia, FJ; Rodriguez-Manas, L; Holzinger, A.
Importance of medical data preprocessing in predictive modeling and risk factor discovery for the frailty syndrome
BMC MED INFORM DECIS. 2019; 19: 33 Doi: 10.1186/s12911-019-0747-6 [OPEN ACCESS]
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Führende Autor*innen der Med Uni Graz
Hassler Andreas Philipp
Holzinger Andreas

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Increasing life expectancy results in more elderly people struggling with age related diseases and functional conditions. This poses huge challenges towards establishing new approaches for maintaining health at a higher age. An important aspect for age related deterioration of the general patient condition is frailty. The frailty syndrome is associated with a high risk for falls, hospitalization, disability, and finally increased mortality. Using predictive data mining enables the discovery of potential risk factors and can be used as clinical decision support system, which provides the medical doctor with information on the probable clinical patient outcome. This enables the professional to react promptly and to avert likely adverse events in advance. Medical data of 474 study participants containing 284 health related parameters, including questionnaire answers, blood parameters and vital parameters from the Toledo Study for Healthy Aging (TSHA) was used. Binary classification models were built in order to distinguish between frail and non-frail study subjects. Using the available TSHA data and the discovered potential predictors, it was possible to design, develop and evaluate a variety of different predictive models for the frailty syndrome. The best performing model was the support vector machine (SVM, 78.31%). Moreover, a methodology was developed, making it possible to explore and to use incomplete medical data and further identify potential predictors and enable interpretability. This work demonstrates that it is feasible to use incomplete, imbalanced medical data for the development of a predictive model for the frailty syndrome. Moreover, potential predictive factors have been discovered, which were clinically approved by the clinicians. Future work will improve prediction accuracy, especially with regard to separating the group of frail patients into frail and pre-frail ones and analyze the differences among them.

Find related publications in this database (Keywords)
Health data analytics
Data mining
Machine learning
Predictive modeling
Risk factor discovery
Data preprocessing
Missing value imputation
Frailty syndrome
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