Observatorio de I+D+i UPM

Memorias de investigación
Communications at congresses:
Predicting the risk of suffering chronic social exclusion with machine learning
Year:2017
Research Areas
  • Artificial intelligence,
  • Personal data
Information
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.
International
No
Congress
Distributed Computing and Artificial Intelligence
960
Place
Oporto
Reviewers
Si
ISBN/ISSN
978-3-319-62410-5
https://doi.org/10.1007/978-3-319-62410-5_16
Start Date
21/06/2017
End Date
23/06/2017
From page
132
To page
139
Predicting the risk of suffering chronic social exclusion with machine learning
Participants
  • Autor: Emilio Serrano Fernandez (UPM)
  • Autor: Pedro del Pozo Jimenez (UPM)
  • Autor: M. Carmen Suarez de Figueroa Baonza (UPM)
  • Autor: Jacinto Gonzalez Pachon (UPM)
  • Autor: Javier Bajo Perez (UPM)
  • Autor: Asuncion de Maria Gomez Perez (UPM)
Research Group, Departaments and Institutes related
  • Creador: Departamento: Inteligencia Artificial
S2i 2020 Observatorio de investigación @ UPM con la colaboración del Consejo Social UPM
Cofinanciación del MINECO en el marco del Programa INNCIDE 2011 (OTR-2011-0236)
Cofinanciación del MINECO en el marco del Programa INNPACTO (IPT-020000-2010-22)