Design of Clinical Support Systems Using Integrated Genetic Algorithm and Support Vector Machine

Authors:
Yung-Fu Chen;Yung-Fa Huang;Xiaoyi Jiang;Yuan-Nian Hsu;Hsuan-Hung Lin
Affiliations:
Department of Health Services Administration, China Medical University, Taichung 40402;Department of Information and Communication Engineering, Chaoyang University of Technology, Taichung 41349;Department of Computer Science, University of Münster, Münster, Germany D-48149 and Department of Health Services Administration, China Medical University, Taichung 40402;Taichung Hospital, Department of Health, Executive Yuan, Taichung 40343 and Department of Health Care Administration, ;Department of MIS, Central Taiwan University of Science and Technology, Taichung, Taiwan 40601
Venue:
CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
Year:
2009

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Abstract

Clinical decision support system (CDSS) provides knowledge and specific information for clinicians to enhance diagnostic efficiency and improving healthcare quality. An appropriate CDSS can highly elevate patient safety, improve healthcare quality, and increase cost-effectiveness. Support vector machine (SVM) is believed to be superior to traditional statistical and neural network classifiers. However, it is critical to determine suitable combination of SVM parameters regarding classification performance. Genetic algorithm (GA) can find optimal solution within an acceptable time, and is faster than greedy algorithm with exhaustive searching strategy. By taking the advantage of GA in quickly selecting the salient features and adjusting SVM parameters, a method using integrated GA and SVM (IGS), which is different from the traditional method with GA used for feature selection and SVM for classification, was used to design CDSSs for prediction of successful ventilation weaning, diagnosis of patients with severe obstructive sleep apnea, and discrimination of different cell types form Pap smear. The results show that IGS is better than methods using SVM alone or linear discriminator.