title: Research on Medical Diagnosis Decision Support System for Acid-base Disturbance Based on Support Vector Machine.
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.
authors.affiliation: Province-Ministry Joint Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability, Hebei University of Technology, Tianjin, China. (phone: +86-22-60201524; e-mail: guoshengrui@163.com).
authors.forename: Lei
authors.initials: L
authors.lastname: Guo
authors.forename: Weili
authors.initials: W
authors.lastname: Yan
authors.forename: Ying
authors.initials: Y
authors.lastname: Li
authors.forename: Youxi
authors.initials: Y
authors.lastname: Wu
authors.forename: Xueqin
authors.initials: X
authors.lastname: Shen