Accepted for/Published in: JMIR AI
Date Submitted: Jul 13, 2022
Open Peer Review Period: Jul 13, 2022 - Sep 7, 2022
Date Accepted: Nov 26, 2022
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited.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.
Chronic Disease Prediction Using Common Data Model
ABSTRACT
Background:
Chronic disease management is a major health issue worldwide.
Objective:
This study suggests the possibility of preemptive management of chronic diseases by predicting the occurrence of chronic diseases using CDM and machine learning. In this study, four major chronic diseases, namely, diabetes, hypertension, hyperlipidemia, and cardiovascular disease, were selected and a model for predicting their occurrence within 10 years was developed.
Methods:
We used 4 algorithms to predict disease occurrence.
Results:
XGBoost presented the highest predictive performance for the 4 diseases (diabetes, hypertension, hyperlipidemia, cardiovascular disease) of 80% or more —0.84 to 0.93 in AUC standards—showing the best performance.
Conclusions:
Through the chronic disease prediction machine learning model developed in this study using RWD-based CDM, even with the National Health Insurance Corporation examination data that can be easily obtained by individuals, the risk of major chronic diseases within 10 years Demonstrate that you can specifically identify your health risk factors.
Citation
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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.