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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Osheroff, J.A., Teich, J.M., Middleton, B., Steen, E.B., Wright, A., Detmer, D.E.: A roadmap for national action on clinical decision support. JAMIA 14, 141–145 (2007)
Osowski, S., Siroic, R., Markiewicz, T., Siwek, K.: Application of support vector machine and genetic algorithm for improved blood cell recognition. IEEE Trans. Instrument & Measurement (2008)
MacIntyre, N.R., Cook, D.J., Ely, E.W.J., Epstein, S.K., Fink, J.B., Heffner, J.E.: Evidence-based guidelines for weaning and discontinuing ventilatory support. Chest 120, 375–395 (2001)
Meade, M., Guyatt, G., Cook, D.J., Griffith, L., Sinuff, T., Kergl, C.: Predicting success in weaning from mechanical ventilation. Chest 120, 400–424 (2001)
Hendrix, H., Kaiser, M.E., Yusen, R.D., Merk, J.: A randomized trial of automated versus conventional protocol-driven weaning from mechanical ventilation following coronary artery bypass surgery. European Journal of Cardio-Thoracic Surgery 29(6), 957–963 (2006)
Young, T., Palta, M., Dempsey, J.: The occurrence of SDB among middle-aged adults. N Engl. J. Med. 328, 1230–1235 (1993)
Flemons, W., Littner, W.M.R.J., Rowley, A., Gay, P., Anderson, W.M., Hudgel, D.W., McEvoy, R.D., Loube, D.I.: Home diagnosis of sleep apnea: A systematic review of the literature. Chest 124, 1543–1579 (2003)
Netzer, N., Eliasson, A.H., Netzer, C., Krisco, D.A.: Overnight Pulse Oximetry for Sleep-Disordered Breathing in Adults-A Review. Chest 120, 625–633 (2001)
Lin, C.L., Yeh, C., Yen, C.W., Hsu, W.H., Hang, L.W.: Comparison of the indices of oxyhemoglobin saturation by pulse oximerty in obstructive sleep apnea hypopnea syndrome. Chest 135(1), 86–93 (2009)
DeMay, R.M.: Common problems in Papanicolaou smear interpretation. Arch. Pathol. Lab Med. 121(3), 229–238 (1997)
Doornewaard, H., van der Schouw, Y.T., van der Graaf, Y., Bos, A.B., van den Tweel, J.G.: Observer variation in cytologic grading for cervical dysplasia of Papanicolaou smears with the PAPNET testing system. Cancer 87(4), 178–183 (1999)
Tucker, J.H.: CERVISCAN: An image analysis system for experiments in automatic cervical smear prescreening. Comput. Biomed. Res. 9(2), 93–107 (1976)
Nunobiki, O., Sato, M., Taniguchi, E., Tang, W., Nakamura, M., Utsunomiya, H., Nakamura, Y., Mori, I., Kakudo, K.: Color image analysis of cervical neoplasia using RGB computer color specification. Anal. Quant. Cytol. Histol. 24(5), 289–294 (2002)
Lam, J.C.M., Lam, B., Lam, C.L., Fong, D., Wang, J.K.L., Tse, H.F., Lam, K.S.L., Ip, M.S.M.: Obstructive sleep apnea and the metabolic syndrome in community-based Chinese adults in Hong Kong. Respiratory Medicine 100, 980–987 (2006)
Huang, P.-C., Chan, Y.-K., Chan, P.-C., Chen, Y.-F., Chen, R.-C., Huang, Y.-R.: Quantitative assessment of pap smear cells by PC-based cytopathologic image analysis system and support vector machine. In: Zhang, D. (ed.) ICMB 2008. LNCS, vol. 4901, pp. 192–199. Springer, Heidelberg (2008)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm
Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 2nd edn. Academic Press, San Dieago (2003)
Yang, H.Y., Hsu, J.C., Chen, Y.F., Jiang, X.Y., Chen, T.S.: Using Support Vector Machine to Construct a Predictive Model for Clinical Decision-Making of Ventilation Weaning. In: 2008 International Joint Conference on Neural Network, pp. 3980–3985. IEEE Press, New York (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, YF., Huang, YF., Jiang, X., Hsu, YN., Lin, HH. (2009). Design of Clinical Support Systems Using Integrated Genetic Algorithm and Support Vector Machine. In: Jiang, X., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2009. Lecture Notes in Computer Science, vol 5702. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03767-2_96
Download citation
DOI: https://doi.org/10.1007/978-3-642-03767-2_96
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03766-5
Online ISBN: 978-3-642-03767-2
eBook Packages: Computer ScienceComputer Science (R0)