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Descripción
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| The ?ght against social exclusion is at the heart of the Europe 2020 strategy: 120 million people are at risk of su ering 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 Le?on. 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 that allows social workers to calculate the risk of a social exclusion case to become chronic through a smartphone. | |
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Internacional
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Si |
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Nombre congreso
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DCAI 2017 |
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Tipo de participación
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960 |
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Lugar del congreso
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Oporto, Portugal |
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Revisores
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Si |
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ISBN o ISSN
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978-3-319-62409-9 |
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DOI
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10.1007/978-3-319-62410-5_16 |
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Fecha inicio congreso
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21/06/2017 |
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Fecha fin congreso
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23/06/2017 |
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Desde la página
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132 |
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Hasta la página
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139 |
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Título de las actas
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DCAI 2017 |