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A Development of a Tailored Follow up Management Model Using the Data Mining Technique on Hypertension
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 Title & Authors
A Development of a Tailored Follow up Management Model Using the Data Mining Technique on Hypertension
Park, Il-Su; Yong, Wang-Sik; Kim, Yu-Mi; Kang, Sung-Hong; Han, Jun-Tae;
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 Abstract
This study used the characteristics of the knowledge discovery and data mining algorithms to develop tailored hypertension follow up management model - hypertension care predictive model and hypertension care compliance segmentation model - for hypertension management using the Korea National Health Insurance Corporation database(the insureds’ screening and health care benefit data). This study validated the predictive power of data mining algorithms by comparing the performance of logistic regression, decision tree, and ensemble technique. On the basis of internal and external validation, it was found that the model performance of logistic regression method was the best among the above three techniques on hypertension care predictive model and hypertension care compliance segmentation model was developed by Decision tree analysis. This study produced several factors affecting the outbreak of hypertension using screening. It is considered to be a contributing factor towards the nation’s building of a Hypertension follow up Management System in the near future by bringing forth representative results on the rise and care of hypertension.
 Keywords
Hypertension follow up management model;data mining;logistic regression;decision tree analysis;
 Language
Korean
 Cited by
1.
데이터마이닝을 이용한 위암 예측모형 개발과 활용,박일수;한준태;강석복;지재훈;

Journal of the Korean Data and Information Science Society, 2010. vol.21. 6, pp.1253-1261
2.
데이터마이닝 기법을 활용한 국민건강보험 상해상병 관리모형 개발,박일수;한준태;손혜숙;강석복;

Journal of the Korean Data and Information Science Society, 2011. vol.22. 3, pp.467-476
3.
An Analysis of the Correlation between Alopecia and Chief Complaints,;;;

Healthcare Informatics Research, 2011. vol.17. 4, pp.253-259 crossref(new window)
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