Title :
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
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;
Conference_Titel :
Computer Communication Control and Automation (3CA), 2010 International Symposium on
Conference_Location :
Tainan
Print_ISBN :
978-1-4244-5565-2
DOI :
10.1109/3CA.2010.5533874