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Predictive Modeling of ICU Healthcare-Associated Infections from Imbalanced Data. Using Ensembles and a Clustering-Based Undersampling Approach

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Faculty of Nursing and Physiotherapy, University of Salamanca, 37007 Salamanca, Spain
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Intensive Care Unit, University Hospital of Salamanca, 37007 Salamanca, Spain
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Department of Computing and Automation, University of Salamanca, 37008 Salamanca, Spain
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Department of Statistics, University of Salamanca, 37007 Salamanca, Spain
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Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(24), 5287; https://doi.org/10.3390/app9245287
Received: 16 October 2019 / Revised: 30 November 2019 / Accepted: 1 December 2019 / Published: 4 December 2019
(This article belongs to the Section Computing and Artificial Intelligence)
Early detection of patients vulnerable to infections acquired in the hospital environment is a challenge in current health systems given the impact that such infections have on patient mortality and healthcare costs. This work is focused on both the identification of risk factors and the prediction of healthcare-associated infections in intensive-care units by means of machine-learning methods. The aim is to support decision making addressed at reducing the incidence rate of infections. In this field, it is necessary to deal with the problem of building reliable classifiers from imbalanced datasets. We propose a clustering-based undersampling strategy to be used in combination with ensemble classifiers. A comparative study with data from 4616 patients was conducted in order to validate our proposal. We applied several single and ensemble classifiers both to the original dataset and to data preprocessed by means of different resampling methods. The results were analyzed by means of classic and recent metrics specifically designed for imbalanced data classification. They revealed that the proposal is more efficient in comparison with other approaches. View Full-Text
Keywords: ensemble classifiers; healthcare-associated infections; ICU infections; imbalanced data; machine learning; oversampling; undersampling ensemble classifiers; healthcare-associated infections; ICU infections; imbalanced data; machine learning; oversampling; undersampling
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Sánchez-Hernández, F.; Ballesteros-Herráez, J.C.; Kraiem, M.S.; Sánchez-Barba, M.; Moreno-García, M.N. Predictive Modeling of ICU Healthcare-Associated Infections from Imbalanced Data. Using Ensembles and a Clustering-Based Undersampling Approach. Appl. Sci. 2019, 9, 5287.

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