Statistics and Deep Belief Network-Based Cardiovascular Risk Prediction
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Jaekwon | - |
dc.contributor.author | Kang, Ungu | - |
dc.contributor.author | Lee, Youngho | - |
dc.date.available | 2020-02-27T18:41:47Z | - |
dc.date.created | 2020-02-06 | - |
dc.date.issued | 2017-07 | - |
dc.identifier.issn | 2093-3681 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/5995 | - |
dc.description.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. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | KOREAN SOC MEDICAL INFORMATICS | - |
dc.relation.isPartOf | HEALTHCARE INFORMATICS RESEARCH | - |
dc.subject | CLASSIFICATION | - |
dc.title | Statistics and Deep Belief Network-Based Cardiovascular Risk Prediction | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000417088300005 | - |
dc.identifier.doi | 10.4258/hir.2017.23.3.169 | - |
dc.identifier.bibliographicCitation | HEALTHCARE INFORMATICS RESEARCH, v.23, no.3, pp.169 - 175 | - |
dc.identifier.kciid | ART002249083 | - |
dc.identifier.scopusid | 2-s2.0-85027006928 | - |
dc.citation.endPage | 175 | - |
dc.citation.startPage | 169 | - |
dc.citation.title | HEALTHCARE INFORMATICS RESEARCH | - |
dc.citation.volume | 23 | - |
dc.citation.number | 3 | - |
dc.contributor.affiliatedAuthor | Kang, Ungu | - |
dc.contributor.affiliatedAuthor | Lee, Youngho | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | Cardiovascular Diseases | - |
dc.subject.keywordAuthor | Deep Belief Network | - |
dc.subject.keywordAuthor | Machine Learning | - |
dc.subject.keywordAuthor | Cardiovascular Risk Prediction | - |
dc.subject.keywordAuthor | KNHANES | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
dc.relation.journalResearchArea | Medical Informatics | - |
dc.relation.journalWebOfScienceCategory | Medical Informatics | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
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