NDLI: Comparison of survival predictions for rats with hemorrhagic shocks using an artificial neural network and support vector machine

Content Provider	IEEE Xplore Digital Library
Author	Jang, Kyung Hwan Yoo, Tae Keun Choi, Joon Yul Nam, Ki Chang Choi, Jae Lim Kwon, Min Kyung Kim, Deok Won
Copyright Year	2011
Description	Author affiliation: Graduate Program in Biomedical Engineering, Yonsei University, Seoul, Korea (Jang, Kyung Hwan; Choi, Jae Lim) \|\| Dept. of Medical Engineering, Yonsei University College of Medicine, Seoul, Korea (Nam, Ki Chang; Kim, Deok Won) \|\| Brain Korea 21 Project for Medical Science, Yonsei University, Seoul, Korea (Choi, Joon Yul; Kwon, Min Kyung) \|\| Yonsei University College of Medicine, Seoul, Korea (Yoo, Tae Keun)
Abstract	Hemorrhagic shock is the cause of one third of deaths resulting from injury in the world. Early diagnosis of hemorrhagic shock makes it possible for physicians to treat patients successfully. The objective of this study was to select an optimal survival prediction model using physiological parameters from rats during our hemorrhagic experiment. These physiological parameters were used for the training and testing of survival prediction models using an artificial neural network (ANN) and support vector machine (SVM). To avoid over-fitting, we chose the optimal survival prediction model according to performance measured by a 5-fold cross validation method. We selected an ANN with three hidden neurons and one hidden layer and an SVM with Gaussian kernel function as a trained survival prediction model. For the ANN model, the sensitivity, specificity, and accuracy of survival prediction were 97.8 ± 3.3 %, 96.3 ± 2.7 %, and 96.8 ± 1.7 %, respectively. For the SVM model, the sensitivity, specificity, and accuracy were 97.5 ± 2.9 %, 99.3 ± 1.1 %, and 98.5 ± 1.2 %, respectively. SVM was preferable to ANN for the survival prediction.
Starting Page	91
Ending Page	94
File Size	528526
Page Count	4
File Format	PDF
ISBN	9781424441211
ISSN	1557170X
e-ISBN	9781457715891
e-ISBN	9781424441228
DOI	10.1109/IEMBS.2011.6089904
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Support vector machines Predictive models Hemorrhaging Electric shock Accuracy Neurons
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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