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A Development of a Tailored Follow up Management Model Using the Data Mining Technique on Hypertension

데이터마이닝 기법을 활용한 맞춤형 고혈압 사후관리 모형 개발

Park, Il-Su;Yong, Wang-Sik;Kim, Yu-Mi;Kang, Sung-Hong;Han, Jun-Tae
박일수;용왕식;김유미;강성홍;한준태

  • Published : 2008.08.31

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

References

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