Background: Early detection of childhood developmental delays is very important for the treatment of disabilities.
Objective: To investigate the possibility of detecting childhood developmental delays leading to disabilities before clinical registration by analyzing big data from a health insurance database.
Methods: In this study, the data from children, individuals aged up to 13 years (n=2412), from the Sample Cohort 2.0 DB of the Korea National Health Insurance Service were organized by age range. Using 6 categories (having no disability, having a physical disability, having a brain lesion, having a visual impairment, having a hearing impairment, and having other conditions), features were selected in the order of importance with a tree-based model. We used multiple classification algorithms to find the best model for each age range. The earliest age range with clinically significant performance showed the age at which conditions can be detected early.
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 mean diagnostic age of 4.99 years.
Conclusions: Using big data analysis, we discovered the possibility of detecting disabilities earlier than clinical diagnoses, which would allow us to take appropriate action to prevent disabilities.
Keywords: big data; classification; early detection of disabilities; feature selection; health insurance.
©Seung-Hyun Jeong, Tae Rim Lee, Jung Bae Kang, Mun-Taek Choi. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 23.11.2020.