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 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.
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Serrano, E., del Pozo-Jiménez, P., Suárez-Figueroa, M.C., González-Pachón, J., Bajo, J., Gómez-Pérez, A. (2018). Predicting the risk of suffering chronic social exclusion with machine learning. In: Omatu, S., Rodríguez, S., Villarrubia, G., Faria, P., Sitek, P., Prieto, J. (eds) Distributed Computing and Artificial Intelligence, 14th International Conference. DCAI 2017. Advances in Intelligent Systems and Computing, vol 620. Springer, Cham. https://doi.org/10.1007/978-3-319-62410-5_16
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DOI: https://doi.org/10.1007/978-3-319-62410-5_16
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