Please use this identifier to cite or link to this item: https://publicaciones.fctunca.edu.py/jspui/handle/123456789/90
Title: Feature Selection with Multivariate Symmetrical Uncertainty to predict Dengue Cases using Deep Learning
Authors: Ortega Velázquez, Marcos Antonio
Gómez Guerrero, Santiago
Keywords: redes neuronales
predicción de casos
dengue
MSU
Issue Date: Oct-2018
Publisher: Universidad Comunera
Series/Report no.: Workshop Ciencia de Datos;1
Abstract: According to the OMS dengue is a viral disease transmitted by the Aedes Aegypti female mosquito, affecting vast areas of the world. In the last 50 years, its incidence in Paraguay has increased, accompanying the persistent migration into the cities [1]. Approximately 80 million cases appear every year in more than 100 countries, and about 2.5 billion people live in countries with endemic dengue. Paraguay is part of this list of countries, as one of the most affected by the disease. According to DGVS since the appearance of dengue in Paraguayan territory there has been a scalar increase in policies, strategies and public health services that prevent and combat the outbreaks. Despite all these efforts, large epidemics were recorded in the 1988-1989; 1999-2000; 2006- 2007 and 2012-2013 periods [1]; and currently there are many cases of the disease in the country. According to the OMS dengue is a viral disease transmitted by the Aedes Aegypti female mosquito, affecting vast areas of the world. In the last 50 years, its incidence in Paraguay has increased, accompanying the persistent migration into the cities [1]. Approximately 80 million cases appear every year in more than 100 countries, and about 2.5 billion people live in countries with endemic dengue. Paraguay is part of this list of countries, as one of the most affected by the disease.
URI: https://publicaciones.fctunca.edu.py/jspui/handle/123456789/90
Appears in Collections:Workshop Ciencia de Datos UCOM 2018

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