| Issue |
ITM Web Conf.
Volume 12, 2017
The 4th Annual International Conference on Information Technology and Applications (ITA 2017)
|
|
|---|---|---|
| Article Number | 05013 | |
| Number of page(s) | 6 | |
| Section | Session 5: Information Processing Methods and Techniques | |
| DOI | https://doi.org/10.1051/itmconf/20171205013 | |
| Published online | 05 September 2017 | |
Optimised Selection of Stroke Biomarker Based on Svm and Information Theory
School of Electronic and Information Engineering, Beihang University,, Beijing, China
wxiang@buaa.edu.cn
littlestone_buaa@163.com
wangxc1992@buaa.edu.cn
wt860122@buaa.edu.cn
With the development of molecular biology and gene-engineering technology, gene diagnosis has been an emerging approach for modern life sciences. Biological marker, recognized as the hot topic in the molecular and gene fields, has important values in early diagnosis, malignant tumor stage, treatment and therapeutic efficacy evaluation. So far, the researcher has not found any effective way to predict and distinguish different type of stroke. In this paper, we aim to optimize stroke biomarker and figure out effective stroke detection index based on SVM (support vector machine) and information theory. Through mutual information analysis and principal component analysis to complete the selection of biomarkers and then we use SVM to verify our model. According to the testing data of patients provided by Xuanwu Hospital, we explore the significant markers of the stroke through data analysis. Our model can predict stroke well. Then discuss the effects of each biomarker on the incidence of stroke.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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