Memorias de investigación
Ponencias en congresos:
Predicting the risk of suffering chronic social exclusion with machine learning
Año:2017
Áreas de investigación
  • Inteligencia artificial,
  • Datos personales
Datos
Descripción
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.
Internacional
No
Nombre congreso
Distributed Computing and Artificial Intelligence
Tipo de participación
960
Lugar del congreso
Oporto
Revisores
Si
ISBN o ISSN
978-3-319-62410-5
DOI
https://doi.org/10.1007/978-3-319-62410-5_16
Fecha inicio congreso
21/06/2017
Fecha fin congreso
23/06/2017
Desde la página
132
Hasta la página
139
Título de las actas
Predicting the risk of suffering chronic social exclusion with machine learning
Esta actividad pertenece a memorias de investigación
Participantes
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Departamento: Inteligencia Artificial