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KMID : 1100520170230030169
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Healthcare Informatics Research
2017 Volume.23 No. 3 p.169 ~ p.175
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Statistics and Deep Belief Network-Based Cardiovascular Risk Prediction
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Kim Jae-Kwon
Kang Un-Gu
Lee Young-Ho
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Abstract
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Objectives: Cardiovascular predictions are related to patients’ quality of life and health. Therefore, a risk prediction model for cardiovascular conditions is needed.
Methods: In this paper, we propose a cardiovascular disease prediction model using the sixth Korea National Health and Nutrition Examination Survey (KNHANES-VI) 2013 dataset to analyze cardiovascular-related health data. First, statistical analysis was performed to find variables related to cardiovascular disease using health data related to cardiovascular disease. Second, a model of cardiovascular risk prediction by learning based on the deep belief network (DBN) was developed.
Results: The proposed statistical DBN-based prediction model showed accuracy and an ROC curve of 83.9% and 0.790, respectively. Thus, the proposed statistical DBN performed better than other prediction algorithms.
Conclusions: The DBN proposed in this study appears to be effective in predicting cardiovascular risk and, in particular, is expected to be applicable to the prediction of cardiovascular disease in Koreans.
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KEYWORD
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Cardiovascular Diseases, Deep Belief Network, Machine Learning, Cardiovascular Risk Prediction, KNHANES
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