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Development and Validation of a Deep Learning Based Diabetes Prediction System Using a Nationwide Population-Based Cohort
- Development and Validation of a Deep Learning Based Diabetes Prediction System Using a Nationwide Population-Based Cohort
- Rhee, Sang Youl; Sung, Ji Min; Kim, Sunhee; Cho, In-Jeong; Lee, Sang-Eun; Chang, Hyuk-Jae
- Ewha Authors
- SCOPUS Author ID
- Issue Date
- Journal Title
- DIABETES & METABOLISM JOURNAL
- DIABETES & METABOLISM JOURNAL vol. 45, no. 4, pp. 515 - 525
- Diabetes mellitus; type 2; Mass screening; Prediabetic state; Prediction
- KOREAN DIABETES ASSOC
- SCIE; SCOPUS; KCI
- Document Type
- Background: Previously developed prediction models for type 2 diabetes mellitus (T2DM) have limited performance. We devel-oped a deep learning (DL) based model using a cohort representative of the Korean population. Methods: This study was conducted on the basis of the National Health Insurance Service-Health Screening (NHIS-HEALS) co-hort of Korea. Overall, 335,302 subjects without T2DM at baseline were included. We developed the model based on 80% of the subjects, and verified the power in the remainder. Predictive models for T2DM were constructed using the recurrent neural net-work long short-term memory (RNN-LSTM) network and the Cox longitudinal summary model. The performance of both models over a 10-year period was compared using a time dependent area under the curve. Results: During a mean follow-up of 10.4 +/- 1.7 years, the mean frequency of periodic health check-ups was 2.9 +/- 1.0 per subject. During the observation period, T2DM was newly observed in 8.7% of the subjects. The annual performance of the model created using the RNN-LSTM network was superior to that of the Cox model, and the risk factors for T2DM, derived using the two mod-els were similar; however, certain results differed. Conclusion: The DL-based T2DM prediction model, constructed using a cohort representative of the population, performs bet-ter than the conventional model. After pilot tests, this model will be provided to all Korean national health screening recipients in the future.
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