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Open Access Highly Accessed Research article

Support vector machine versus logistic regression modeling for prediction of hospital mortality in critically ill patients with haematological malignancies

T Verplancke1*, S Van Looy2, D Benoit1, S Vansteelandt3, P Depuydt1, F De Turck2 and J Decruyenaere1

Author Affiliations

1 Department of Intensive Care Medicine, Ghent University Hospital, Faculty of Medicine, Ghent University, Ghent, Belgium

2 Department of Information Technology, Faculty of Engineering, Ghent University, Ghent, Belgium

3 Department of Applied Mathematics and Computer Science, Faculty of Sciences, Ghent University, Ghent, Belgium

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BMC Medical Informatics and Decision Making 2008, 8:56  doi:10.1186/1472-6947-8-56

Published: 5 December 2008

Abstract

Background

Several models for mortality prediction have been constructed for critically ill patients with haematological malignancies in recent years. These models have proven to be equally or more accurate in predicting hospital mortality in patients with haematological malignancies than ICU severity of illness scores such as the APACHE II or SAPS II [1]. The objective of this study is to compare the accuracy of predicting hospital mortality in patients with haematological malignancies admitted to the ICU between models based on multiple logistic regression (MLR) and support vector machine (SVM) based models.

Methods

352 patients with haematological malignancies admitted to the ICU between 1997 and 2006 for a life-threatening complication were included. 252 patient records were used for training of the models and 100 were used for validation. In a first model 12 input variables were included for comparison between MLR and SVM. In a second more complex model 17 input variables were used. MLR and SVM analysis were performed independently from each other. Discrimination was evaluated using the area under the receiver operating characteristic (ROC) curves (± SE).

Results

The area under ROC curve for the MLR and SVM in the validation data set were 0.768 (± 0.04) vs. 0.802 (± 0.04) in the first model (p = 0.19) and 0.781 (± 0.05) vs. 0.808 (± 0.04) in the second more complex model (p = 0.44). SVM needed only 4 variables to make its prediction in both models, whereas MLR needed 7 and 8 variables in the first and second model respectively.

Conclusion

The discriminative power of both the MLR and SVM models was good. No statistically significant differences were found in discriminative power between MLR and SVM for prediction of hospital mortality in critically ill patients with haematological malignancies.