Abstract
The perception that every person has about her health is very important for health, health evolution and therefore for enjoying a healthy life. So, it is important to know what the factors that most influence self-rated health are. If these factors are known, changes that have long term impact on people’s health can be proposed, even for unhealthy people. The present paper performs a preliminary study in that direction, analyzing the causes of self-rated health from a couple of datasets: one private with 4848 people and one public with 802 people. These datasets have been analyzed to find out what socioeconomic, biological and environmental factors have more influence on health status and a dashboard has been created to allow analyzing the data interactively. Results show some factors that influence self-rated health such as chronic diseases, limitation in daily activities and depression were important.
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Acknowledgments
We would like to thank to the Departament de Salut de la Generalitat de Catalunya for sharing the ESCA dataset. Without it, this research would have been impossible. This research have been partially funded by Recercaixa (“la Caixa” Foundation) and by the Catalonia Competitiveness Agency (ACC1Ó) and the eHealth Center from the UOC.
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Subirats, L., Piñeiro, E., Conesa, J., Armayones, M. (2019). Towards the Analysis of Self-rated Health Using Supervised Machine Learning and Business Intelligence. In: Xhafa, F., Barolli, L., Greguš, M. (eds) Advances in Intelligent Networking and Collaborative Systems. INCoS 2018. Lecture Notes on Data Engineering and Communications Technologies, vol 23. Springer, Cham. https://doi.org/10.1007/978-3-319-98557-2_22
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DOI: https://doi.org/10.1007/978-3-319-98557-2_22
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