Statistics and Deep Belief Network-Based Cardiovascular Risk Prediction.
10.4258/hir.2017.23.3.169
- Author:
Jaekwon KIM
1
;
Ungu KANG
;
Youngho LEE
Author Information
1. Department of Computer and Information Engineering, Inha University, Incheon, Korea.
- Publication Type:Original Article
- Keywords:
Cardiovascular Diseases;
Deep Belief Network;
Machine Learning;
Cardiovascular Risk Prediction;
KNHANES
- MeSH:
Cardiovascular Diseases;
Dataset;
Korea;
Learning;
Machine Learning;
Nutrition Surveys;
Quality of Life;
ROC Curve
- From:Healthcare Informatics Research
2017;23(3):169-175
- CountryRepublic of Korea
- Language:English
-
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.
