Abstract
In this paper we present a new methodology based on machine learning methods that allows to select from the available features that define a problem, a subset with the most discriminant ones to outperform a classification. As an application, we have used it to select, from the attributes of the optic nerve obtained by Heidelberg Retina Tomograph II, the most informative ones to discriminate between glaucoma and non-glaucoma. Applying this methodology we have identified 7 attributes from the original 103 attributes, improving the ROC area a 2.38%. These attributes match to a large extent with the most informative ones according to the ophthalmologist’s experience in clinic as well as the literature.
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García-Morate, D., Simón-Hurtado, A., Vivaracho-Pascual, C., Antón-López, A. (2009). A New Methodology for Feature Selection Based on Machine Learning Methods Applied to Glaucoma. In: Cabestany, J., Sandoval, F., Prieto, A., Corchado, J.M. (eds) Bio-Inspired Systems: Computational and Ambient Intelligence. IWANN 2009. Lecture Notes in Computer Science, vol 5517. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02478-8_130
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DOI: https://doi.org/10.1007/978-3-642-02478-8_130
Publisher Name: Springer, Berlin, Heidelberg
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