Affiliation:
1. Accenture Japan Ltd
2. Department of Epidemiology and Prevention, National Center for Global Health and Medicine, Tokyo, Japan
Abstract
Objectives
: Although numerous risk prediction models have been proposed, few such models have been developed using neural network-based survival analysis. We developed risk prediction models for three cardiovascular disease risk factors (diabetes mellitus, hypertension, and dyslipidemia) among a working-age population in Japan using DeepSurv, a deep feed-forward neural network.
Methods
: Data were obtained from the Japan Epidemiology Collaboration on Occupational Health Study. A total of 51 258, 44 197, and 31 452 individuals were included in the development of risk models for diabetes mellitus, hypertension, and dyslipidemia, respectively; two-thirds of whom were used to develop prediction models, and the rest were used to validate the models. We compared the performances of DeepSurv-based models with those of prediction models based on the Cox proportional hazards model.
Results
: The area under the receiver-operating characteristic curve was 0.878 [95% confidence interval (CI) = 0.864–0.892] for diabetes mellitus, 0.835 (95% CI = 0.826–0.845) for hypertension, and 0.826 (95% CI = 0.817–0.835) for dyslipidemia. Compared with the Cox proportional hazards-based models, the DeepSurv-based models had better reclassification performance [diabetes mellitus: net reclassification improvement (NRI) = 0.474, P ≤ 0.001; hypertension: NRI = 0.194, P ≤ 0.001; dyslipidemia: NRI = 0.397, P ≤ 0.001] and discrimination performance [diabetes mellitus: integrated discrimination improvement (IDI) = 0.013, P ≤ 0.001; hypertension: IDI = 0.007, P ≤ 0.001; and dyslipidemia: IDI = 0.043, P ≤ 0.001].
Conclusion
: This study suggests that DeepSurv has the potential to improve the performance of risk prediction models for cardiovascular disease risk factors.
Publisher
Ovid Technologies (Wolters Kluwer Health)
Subject
Cardiology and Cardiovascular Medicine,Physiology,Internal Medicine
Cited by
1 articles.
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