Towards the Analysis of Self-rated Health Using Supervised Machine Learning and Business Intelligence View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2019

AUTHORS

Laia Subirats , Estefania Piñeiro , Jordi Conesa , Manuel Armayones

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. More... »

PAGES

245-254

References to SciGraph publications

Book

TITLE

Advances in Intelligent Networking and Collaborative Systems

ISBN

978-3-319-98556-5
978-3-319-98557-2

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-98557-2_22

DOI

http://dx.doi.org/10.1007/978-3-319-98557-2_22

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1106330883


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