Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Apr 30, 2020
Date Accepted: Oct 30, 2020
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Big data analysis for early detection of disabilities using health insurance data
ABSTRACT
Background:
Early detection of disability is very important for the prevention and treatment of disabilities.
Objective:
To investigate the possibility of detecting disabilities before registration by analyzing big data from a health insurance database.
Methods:
In this study, health insurance data extracted from the Sample Cohort 2.0 DB of the Korea National Health Insurance Service on 2,286 subjects aged 0–13 years were analyzed. Using five types of disability categories as labels, features were selected in order of most importance based on the tree model. We used multi-class classification algorithms of supervised learning to find the best model for the early detection of disabilities.
Results:
The disability detection model showed that it was possible to detect disabilities with significant accuracy even at the age of 4 years, about a year earlier than the average diagnostic age of 4.99 years.
Conclusions:
Disabilities may be detected earlier than clinical diagnoses by using big data analysis, and appropriate measures may be taken to prevent them.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.