Predicting the risk of suffering chronic social exclusion with machine learning

Serrano Fernández, Emilio and Pozo Jiménez, Pedro del and Suárez de Figueroa Baonza, María del Carmen and González Pachón, Jacinto and Bajo Pérez, Javier and Gómez Pérez, Asunción de María (2017). Predicting the risk of suffering chronic social exclusion with machine learning. In: "14th International Conference on Distributed Computing and Artificial Intelligence (DCAI 2017)", 21-23 Jun 2017, Oporto, Portugal. ISBN 978-3-319-62409-9. pp. 132-139.

Description

Title: Predicting the risk of suffering chronic social exclusion with machine learning
Author/s:
  • Serrano Fernández, Emilio
  • Pozo Jiménez, Pedro del
  • Suárez de Figueroa Baonza, María del Carmen
  • González Pachón, Jacinto
  • Bajo Pérez, Javier
  • Gómez Pérez, Asunción de María
Item Type: Presentation at Congress or Conference (Article)
Event Title: 14th International Conference on Distributed Computing and Artificial Intelligence (DCAI 2017)
Event Dates: 21-23 Jun 2017
Event Location: Oporto, Portugal
Title of Book: Distributed Computing and Artificial Intelligence, 14th International Conference (DCAI 2017)
Date: 2017
ISBN: 978-3-319-62409-9
Subjects:
Freetext Keywords: Social exclusion; Social services; Data analysis; Machine learning; Data mining
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

The fght against social exclusion is at the heart of the Europe 2020 strategy: 120 million people are at risk of sufering this condition in the EU. Risk prediction models are widely used in insurance companies and health services. However, the use of these models to allow an early detection of social exclusion by social workers is not a common practice. This paper describes a data analysis of over 16K cases with over 60 predictors from the Spanish region of Castilla y León. The use of machine learning paradigms such as logistic regression and random forest makes possible a high precision in predicting chronic social exclusion. The paper is complemented with a responsive web available online that allows social workers to calculate the risk of a social exclusion case to become chronic through a smartphone.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainTIN2016-78011-C4-4-RUnspecifiedUniversidad Politécnica de MadridDatos 4.0: retos y soluciones

More information

Item ID: 50337
DC Identifier: http://oa.upm.es/50337/
OAI Identifier: oai:oa.upm.es:50337
Official URL: https://dblp.org/db/conf/dcai/dcai2017.html
Deposited by: Memoria Investigacion
Deposited on: 05 Jun 2019 09:20
Last Modified: 05 Jun 2019 09:20
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