- Volume 14 Issue 2
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Development of Prediction Model for Prevalence of Metabolic Syndrome Using Data Mining: Korea National Health and Nutrition Examination Study
국민건강영양조사를 활용한 대사증후군 유병 예측모형 개발을 위한 융복합 연구: 데이터마이닝을 활용하여
- Kim, Han-Kyoul (Department of Public Health Science, Graduate School BK21 Plus Program in Public Health Science, Korea University) ;
- Choi, Keun-Ho (Korea Worker's Compensation & Welfare Service, Labor Welfare Research Institute, Research Department) ;
- Lim, Sung-Won (School of Health Policy and Management, College of Health Science, Korea University) ;
- Rhee, Hyun-Sill (Department of Public Health Science, Graduate School BK21 Plus Program in Public Health Science, Korea University)
- 김한결 (고려대학교 대학원 보건과학과 BK21 플러스 인간생명-상호작용 융복합 사업단) ;
- 최근호 (근로복지공단 근로복지연구원 조사연구부 통계분석2팀) ;
- 임성원 (고려대학교 일반대학원 보건과학과) ;
- 이현실 (고려대학교 대학원 보건과학과 BK21 플러스 인간생명-상호작용 융복합 사업단)
- Received : 2016.01.01
- Accepted : 2016.02.20
- Published : 2016.02.28
The purpose of this study is to investigate the attributes influencing the prevalence of metabolic syndrome and develop the prediction model for metabolic syndrome over 40-aged people from Korea Health and Nutrition Examination Study 2012. The researcher chose the attributes for prediction model through literature review. Also, we used the decision tree, logistic regression, artificial neural network of data mining algorithm through Weka 3.6. As results, social economic status factors of input attributes were ranked higher than health-related factors. Additionally, prediction model using decision tree algorithm showed finally the highest accuracy. This study suggests that, first of all, prevention and management of metabolic syndrome will be approached by aspect of social economic status and health-related factors. Also, decision tree algorithms known from other research are useful in the field of public health due to their usefulness of interpretation.
Grant : BK21플러스
Supported by : 고려대학교
- E. S. Chung, An analysis of the Relationship between Depression and Menopausal Syndromes in Women at Mid life. Korean J Women Health Nurs, Vol. 3, No. 2. pp.230-240, 1997.
- F. Firouzi, M. Rashidi, S. Hashemi, M. Kangavari, A. Bahari, N. E. Daryani, M. M. Emam, N. Naderi, H. M. Shalmani, A. Farnood, M. Zali, A decision tree-based approach for determining low bone mineral density in inflammatory bowel disease using PWEKA software. European journal of gastroenterology &hepatology, Vol. 19, No. 12, pp. 1075-1081, 2007. https://doi.org/10.1097/MEG.0b013e3282202bb8
- F. S. de Edelenyi, L. Goumidi, S. Bertrais, C. Phillips, R. MacManus, H. Roche, R. Planells, D. Lairon, Prediction of the metabolic syndrome status based on dietary and genetic parameters, using Random Forest. Genes&nutrition, Vol. 3, No. 3-4, pp. 173-176, 2008. https://doi.org/10.1007/s12263-008-0097-y
- G. F. Marquezine, C. M. Oliveira, A. C. Pereira, J. E. Krieger, J. G. Mill, Metabolic syndrome determinants in an urban population from Brazil: social class andgender-specific interaction. International journal of cardiology, Vol. 129, NO. 2, pp.259-265, 2008. https://doi.org/10.1016/j.ijcard.2007.07.097
- G. J. Kim, J. S. Han, Chronic Disease Management using Smart Mobile Device, JOURNAL OF DIGITAL CONVERGENCE, Vol. 12, No. 4, pp. 335-342, 2014.
- J. Quentin-Trautvetter, P. Devos, A. Duhamel, R. Beuscart, Assessing association rules and decision trees on analysis of diabetes data from the DiabCare program in France. Studies in health technology and informatics, Vol. 90, pp.557-561. 2001.
- J. W. Chang, S. C. Sung, The effect of applying u-health system on metabolic syndrome management of elderly, JOURNAL OF DIGITAL CONVERGENCE, Vol. 11, No. 11, pp. 553-560, 2013. https://doi.org/10.14400/JDPM.2013.11.11.553
- K. D. Ko, B. Cho, W. C. Lee, H. W. Lee, H. K. Lee, & B. J. Oh, Obesity Explains Gender Differences in the Association Between Education Level and Metabolic Syndrome in South Korea The Results From the Korean National Health and Nutrition Examination Survey 2010. Asia-Pacific Journal of Public Health, Vol. 27, No, 2, pp.630-639, 2015. https://doi.org/10.1177/1010539513488624
- M. Dash, H. Liu, Feature selection for classification. Intelligent data analysis, Vol. 1, No. 3, pp. 131-156, 1997. https://doi.org/10.1016/S1088-467X(97)00008-5
- M. J. Gage, R. Schwarzkopf, M. Abrouk, J. D. Slover, Impact of metabolic syndrome on perioperative complication rates after total joint arthroplasty surgery. The Journal of arthroplasty, Vol. 29, No. 9, pp.1842-1845, 2014. https://doi.org/10.1016/j.arth.2014.04.009
- S. E. Ramsay, P. H. Whincup, R. Morris, L. Lennon, S. Wannamethee, Is socioeconomic position related to the prevalence of metabolic syndrome? Influence of social class across the life course in a population-based study of older men. Diabetes care, Vol. 31, No. 12, pp.2380-2382, 2008. https://doi.org/10.2337/dc08-1158
- S. H. Chung, Y. M. Suh, Development of a Medial Care Cost Prediction Model for Cancer Patients Using Case-Based Reasoning, Asia Pacific Journal of Information Systems, Vol. 16, No. 2, pp. 69-84, 2006.
- Y. S. Seo, S. H. Kang, A Convergence Study in the Severity-adjusted Mortality Ratio on inpatients with multiple chronic conditions, JOURNAL OF DIGITAL CONVERGENCE, Vol. 13, No. 12, pp. 245-257, 2015. https://doi.org/10.14400/JDC.2015.13.12.245
- American Heart Association, About Metabolic Syndrome, American Heart Association Home page, 2014,http://www.heart.org/HEARTORG/Conditions/More/MetabolicSyndrome/About-Metabolic-Syndrome_UCM_301920_Article.jsp, May 14.
- Donghyun Kim, Seoksoo Kim, "Design of Key Tree-based Management Scheme for Healthcare Information Exchange in Convergent u-Healthcare Service ", Journal of the Korea Convergence Society, Vol. 6, No. 6, pp. 81-86, 2015. https://doi.org/10.15207/JKCS.2015.6.6.081
- Young-Sook Kwon, "Necessity of the Development of a Web-based Obesity Management Program to Prevent Metabolic Syndrome of the Workers", Journal of the Korea Convergence Society, Vol. 5, No. 4, pp. 121-127, 2014. https://doi.org/10.15207/JKCS.2014.5.4.121
- H. M. Haught, J. P. Rose, J. A. Brown, Social-class indicators differentially predict engagement inprevention vs. detection behaviours. Psychology&Health, Vol. 31, No. 1, pp. 21-39, 2016. https://doi.org/10.1080/08870446.2015.1068313
- H. S. Chang, A Study on Weight Control Behaviour, Eating Habits and Health-related Life Habits According to Obesity Degree of Teacher in Jeonbuk Province, Korea. Journal of The Korean Society of Dietary Culture, Vol. 30, No.1 , pp.105-117. 2015. https://doi.org/10.7318/KJFC/2015.30.1.105