An excellent mortality prediction model based on support vector machine (SVM)-a pilot study

Author

Chan, Chien-Lung ; Chen, Chia-Li ; Ting, Hsien-Wei

Author_Institution

Dept. of Inf. Manage., Yuan Ze Univ., Chungli, Taiwan

Volume

1

fYear

2010

fDate

5-7 May 2010

Firstpage

134

Lastpage

137

Abstract

Intensive care is one of the most important components of the modern medical system. Healthcare professionals need to utilize intensive care resources effectively. Mortality prediction models help physicians decide which patients require intensive care the most and which do not. This pilot study retrospectively collected data on 695 patients admitted to intensive care units and constructed a novel mortality prediction model with support vector machine (SVM). The accuracy of new model is good. The precision rate is 0.899. The recall rate is 0.902. The F-Measure is 0.899. The ROC curve is 0.932. This new model can support the physician´s in intensive care decision making.

Keywords

decision making; health care; support vector machines; SVM; healthcare professional; intensive care decision making; medical system; mortality prediction model; support vector machine; Biomedical imaging; Decision making; Health information management; Hospitals; Medical diagnostic imaging; Physiology; Predictive models; Support vector machine classification; Support vector machines; Surgery; intensive care; medical decision making; mortality prediction model; support vector machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Communication Control and Automation (3CA), 2010 International Symposium on

Conference_Location

Tainan

Print_ISBN

978-1-4244-5565-2

Type

conf

DOI

10.1109/3CA.2010.5533874

Filename

5533874