Healthc Inform Res.  2017 Jul;23(3):169-175. 10.4258/hir.2017.23.3.169.

Statistics and Deep Belief Network-Based Cardiovascular Risk Prediction

• Kim J
• Kang U
• Lee Y

• KMID: 2403306
• DOI: http://doi.org/10.4258/hir.2017.23.3.169

Abstract

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.

Keyword

Cardiovascular Diseases; Deep Belief Network; Machine Learning; Cardiovascular Risk Prediction; KNHANES

MeSH Terms

Cardiovascular Diseases
Dataset
Korea
Learning
Machine Learning
Nutrition Surveys
Quality of Life
ROC Curve

Figure

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  Figure 1 Study design.

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  Figure 2 Data preprocessing.

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  Figure 3 Confusion matrix.

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  Figure 4 Restricted Boltzmann machine.

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  Figure 5 Deep belief network.

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  Figure 6 Deep belief network: (A) unsupervised learning, (B) supervised learning. SBP: systolic blood pressure, DBP: diastolic blood pressure, HDL: high-density lipoprotein.

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  Figure 7 Sensitivity results.

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  Figure 8 Specificity results.

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  Figure 9 Accuracy results. NB: naïve Bayesian, LR: logistics regression, BPN: backpropagation network, SVM: support vector machine, RF: random forest, DBN: deep belief network.

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  Figure 10 ROC curve result. NB: naïve Bayesian, LR: logistics regression, BPN: backpropagation network, SVM: support vector machine, RF: random forest, DBN: deep belief network.

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