Toward proactive social inclusion powered by machine learning

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.