A New Methodology for Feature Selection Based on Machine Learning Methods Applied to Glaucoma

Authors:
Diego García-Morate;Arancha Simón-Hurtado;Carlos Vivaracho-Pascual;Alfonso Antón-López
Affiliations:
MetaEmotion, Parque Científico,;Department of Computer Science, E.T.S.I. Informática,;Department of Computer Science, E.T.S.I. Informática,;IOBA (Institute of Ophthalmology and Visual Sciences), University of Valladolid, Valladolid, Spain 47011
Venue:
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Year:
2009

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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.