A survey of machine learning approaches for age related macular degeneration diagnosis and prediction

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dc.contributor.author Martínez Velasco, Antonieta Teodora
dc.contributor.author Martínez Villaseñor, María de Lourdes Guadalupe
dc.contributor.other Campus Ciudad de México
dc.date.accessioned 2019-05-23T18:20:55Z
dc.date.available 2019-05-23T18:20:55Z
dc.date.issued 2018
dc.identifier.citation Martínez Velasco, A. T. y Martínez Villaseñor, M. de L. G. (2018). A survey of machine learning approaches for age related macular degeneration diagnosis and prediction. En: Castro, F, Miranda-Jiménez, S. y González-Mendoza, M. (editores). 16th Mexican international conference on artificial intelligence, MICAI 2017 : Enseneda, Mexico, October 23-28, 2017 : proceedings Part 1, Advances in soft computing, (Lecture notes in computer science, vol. 10632), (pp. 254-266). Cham : Springer. DOI: 10.1007/978-3-030-02837-4_21 es_ES, en_US
dc.identifier.isbn 9783030028367 es_ES, en_US
dc.identifier.issn 0302-9743 es_ES, en_US
dc.identifier.uri http://scripta.up.edu.mx/xmlui/handle/123456789/4838
dc.identifier.uri http://dx.doi.org/10.1007/978-3-030-02837-4_21
dc.description.abstract Age Related Macular Degeneration (AMD) is a complex disease caused by the interaction of multiple genes and environmental factors. AMD is the leading cause of visual dysfunction and blindness in developed countries, and a rising cause in underdeveloped countries. Currently, retinal images are studied in order to identify drusen in the retina. The classification of these images allows to support the medical diagnosis. Likewise, genetic variants and risk factors are studied in order to make predictive studies of the disease, which are carried out with the support of statistical tools and, recently, with Machine Learning (ML) methods. In this paper, we present a survey of studies performed in complex diseases under both approaches, especially for the case of AMD. We emphasize the approach based on the genetic variants of individuals, as it is a support tool for the prevention of AMD. According to the vision of personalized medicine, disease prevention is a priority to improve the quality of life of people and their families, as well as to avoid the inherent health burden. © Springer Nature Switzerland AG 2018. es_ES, en_US
dc.language.iso eng
dc.publisher Springer Verlag es_ES, en_US
dc.relation.ispartof REPOSITORIO SCRIPTA es_ES, en_US
dc.relation.ispartof OPENAIRE es_ES, en_US
dc.relation.ispartofseries Lecture Notes in Computer Science
dc.rights Acceso Embargado es_ES, en_US
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0 es_ES, en_US
dc.rights.uri http://www.sherpa.ac.uk/romeo/issn/0302-9743/
dc.source 16th Mexican international conference on artificial intelligence, MICAI 2017 : Enseneda, Mexico, October 23-28, 2017 : proceedings Part 1, Advances in soft computing
dc.subject AMD es_ES, en_US
dc.subject Automated diagnosis es_ES, en_US
dc.subject Machine learning es_ES, en_US
dc.subject Pattern recognition es_ES, en_US
dc.subject Predictive diagnosis es_ES, en_US
dc.subject Artificial intelligence es_ES, en_US
dc.subject Classification (of information) es_ES, en_US
dc.subject Computer aided diagnosis es_ES, en_US
dc.subject Medical imaging es_ES, en_US
dc.subject Ophthalmology es_ES, en_US
dc.subject Pattern recognition es_ES, en_US
dc.subject Soft computing es_ES, en_US
dc.subject Statistical mechanics es_ES, en_US
dc.subject Surveys es_ES, en_US
dc.subject Age-related macular degeneration es_ES, en_US
dc.subject Automated diagnosis es_ES, en_US
dc.subject Developed countries es_ES, en_US
dc.subject Disease prevention es_ES, en_US
dc.subject Environmental factors es_ES, en_US
dc.subject Machine learning approaches es_ES, en_US
dc.subject Personalized medicines es_ES, en_US
dc.subject Visual dysfunctions es_ES, en_US
dc.subject Learning systems es_ES, en_US
dc.subject.classification INGENIERÍA Y TECNOLOGÍA
dc.subject.classification Ingeniería
dc.title A survey of machine learning approaches for age related macular degeneration diagnosis and prediction es_ES, en_US
dc.type Contribución a congreso es_ES, en_US
dcterms.audience Investigadores
dcterms.audience Estudiantes
dcterms.audience Maestros
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