Abstract
A comparative analysis of 14 classic and 23 novel mathematical features for diagnosing lung function in infants is presented. The data set comprises tidal breathing flow volume loops of 195 spontaneously breathing infants aged 3 to 24 months, with 9 known breathing problems (diseases). The data set is sparse. Diagnostic power was evaluated using support vector machines featuring both polynomial and Gaussian kernels in a rigorous experimental setting (100 runs for random splits of data into the training set (90% of data) and test set (10% of data)). Novel features achieve lower error rates than the old ones.
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References
Operators Manual: 2600 Pediatric Pulmonary Function Laboratory, SensorMedics Corporation, Yorba Linda, CA 92687, USA (1991)
Leonhardt, S.: Automatisierte Lungenfunktionsdiagnose bei spontanatmenden Säuglingen (Automated Lung Function Diagnosis in Spontaneously Breathing Infants, in German), Shaker Verlag, Aachen, Germany (2001)
Buehl, A., Zoefel, P.: SPSS fuer Windows, Version 6.1, 2. Auflage. Addison-Wesley Publishing Company, Bonn (1995)
Kecman, V.: Learning and Soft Computing, Support Vector Machines, Neural Networks, and Fuzzy Logic Models, The MIT Press, Cambridge, MA (2001), http://www.support-vector.ws
Vogt, M., Kecman, V.: Chapter Active-Set Methods for Support Vector Machines. In: L. Wang (ed.) a Springer-Verlag book, Support Vector Machines: Theory and Applications, Series: Studies in Fuzziness and Soft Computing, vol. 177, pp. 133–158 (2005)
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© 2007 Springer-Verlag Berlin Heidelberg
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Leonhardt, S., Kecman, V. (2007). Novel Features for Automated Lung Function Diagnosis in Spontaneously Breathing Infants. In: Bellazzi, R., Abu-Hanna, A., Hunter, J. (eds) Artificial Intelligence in Medicine. AIME 2007. Lecture Notes in Computer Science(), vol 4594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73599-1_26
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DOI: https://doi.org/10.1007/978-3-540-73599-1_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73598-4
Online ISBN: 978-3-540-73599-1
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