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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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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