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Research on Medical Diagnosis Decision Support System for Acid-base Disturbance Based on Support Vector Machine.

Authors
  • Guo, Lei
  • Yan, Weili
  • Li, Ying
  • Wu, Youxi
  • Shen, Xueqin
Type
Published Article
Journal
Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Publication Date
Jan 01, 2005
Volume
3
Pages
2413–2416
Identifiers
PMID: 17282724
Source
Medline
License
Unknown

Abstract

Support Vector Machine (SVM) is a new learning technique based on Statistical Learning Theory (SLT). In this paper, a Medical Diagnosis Decision System (MDDSS) based on SVM has been established to intellectively diagnose 4 types of acid-base disturbance. SVM was originally developed for two-class classification. It is extended to solve multi-class classification problem named hierarchical SVM with clustering algorithm based on stepwise decomposition. Compared with other classical classification techniques, SVM not only has more solid theoretical foundation, it also has greater generalization ability as our experiment demonstrates. Thus, SVM exhibits its great potential in MDDSS.

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