SVM-based decision support system for clinic aided tracheal intubation predication with multiple features

Authors:
Qing Yan;Hongmei Yan;Fei Han;Xinchuan Wei;Tao Zhu
Affiliations:
School of Life Science and Technology, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, Chengdu 610054, PR China;School of Life Science and Technology, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, Chengdu 610054, PR China;School of Life Science and Technology, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, Chengdu 610054, PR China;Department of Anaesthesiology, West China Hospital of Sichuan University, Chengdu 610064, PR China;Department of Anaesthesiology, West China Hospital of Sichuan University, Chengdu 610064, PR China
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

During routine anaesthesia, an airway physical examination should be conducted in all patients to estimate whether tracheal intubation is easy or difficult. In clinic, some anaesthetists usually do this by examining single item although most of the specialists agree that full consideration of multiple features of airway physical examination rather than single one would enable anaesthetists to improve the prediction accuracy when encountering a difficult airway. The application of machine learning tools has shown its advantage in medical aided decision. The purpose of this study is to construct a medical decision support system based on support vector machines with 13 physical features for tracheal intubation predication ahead of anaesthesia. A total of 264 medical records collected from patients suffering from a variety of diseases ensure the generalization performance of the decision system. Moreover, the robustness of the proposed system is examined using 4-fold cross-validation method and results show the SVM-based decision support system can achieve average classification accuracy at 90.53%, manifesting its great application prospect of supporting clinic aided diagnosis with full consideration of multiple features of airway physical examination.