Importance of medical data preprocessing in predictive modeling and risk factor discovery for the frailty syndrome

BMC Medical Informatics and Decision Making. 2019;19(1):1-17 DOI 10.1186/s12911-019-0747-6


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Journal Title: BMC Medical Informatics and Decision Making

ISSN: 1472-6947 (Online)

Publisher: BMC

LCC Subject Category: Medicine: Medicine (General): Computer applications to medicine. Medical informatics

Country of publisher: United Kingdom

Language of fulltext: English

Full-text formats available: PDF, HTML



Andreas Philipp Hassler (Holzinger Group, HCI-KDD, Institute for Medical Informatics/Statistics, Medical University Graz)

Ernestina Menasalvas (Center for Biomedical Technology, Universidad Politecnica de Madrid)

Francisco José García-García (Division of Geriatric Medicine, Virgen del Valle Geriatric Hospital)

Leocadio Rodríguez-Mañas (Division of Geriatric Medicine, University Hospital of Getafe)

Andreas Holzinger (Holzinger Group, HCI-KDD, Institute for Medical Informatics/Statistics, Medical University Graz)


Open peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 23 weeks


Abstract | Full Text

Abstract Background 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. Methods 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. Results 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. Conclusions 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.