Toward proactive social inclusion powered by machine learning

Serrano Fernández, Emilio and Suárez-Figueroa, Mari Carmen and González Pachón, Jacinto and Gómez-Pérez, A. (2019). Toward proactive social inclusion powered by machine learning. "Knowledge and Information Systems", v. 58 (n. 3); pp. 651-667. ISSN 0219-1377. https://doi.org/10.1007/s10115-018-1230-x.

Description

Title: Toward proactive social inclusion powered by machine learning
Author/s:
  • Serrano Fernández, Emilio
  • Suárez-Figueroa, Mari Carmen
  • González Pachón, Jacinto
  • Gómez-Pérez, A.
Item Type: Article
Título de Revista/Publicación: Knowledge and Information Systems
Date: March 2019
ISSN: 0219-1377
Volume: 58
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 fight against social exclusion is at the heart of the Europe 2020 strategy: 120 million people are at risk of suffering 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 16 K 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: around 90% in the most conservative predictions. This prediction models offer a quick rule of thumb that can detect citizens who are in danger of been excluded from the society beyond a temporary situation, allowing social workers to further study these cases.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainTIN2016-78011-C4-4-RUnspecifiedUnspecifiedR&D Proyect Datos 4.0: Retos y soluciones

More information

Item ID: 72404
DC Identifier: https://oa.upm.es/72404/
OAI Identifier: oai:oa.upm.es:72404
DOI: 10.1007/s10115-018-1230-x
Official URL: https://link.springer.com/article/10.1007/s10115-018-1230-x
Deposited by: Biblioteca Facultad de Informatica
Deposited on: 19 Jan 2023 11:15
Last Modified: 19 Jan 2023 11:15
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