A Data-Driven Procedure of Providing a Health Promotion Program for Hypertension Prevention

Junghye Lee (), Ryeok-Hwan Kwon (), Hyung Woo Kim (), Sung-Hong Kang (), Kwang-Jae Kim () and Chi-Hyuck Jun ()
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Junghye Lee: Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang 37673, Korea; School of Management Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea
Ryeok-Hwan Kwon: Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang 37673, Korea
Hyung Woo Kim: Department of Internal Medicine, Yonsei University College of Medicine, Seoul 03722, Korea
Sung-Hong Kang: Department of Health Policy and Management, Inje University, Gimhae 50834
Kwang-Jae Kim: Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang 37673, Korea
Chi-Hyuck Jun: Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang 37673, Korea

Service Science, 2018, vol. 10, issue 3, 289-301

Abstract: We propose a two-step procedure based on data analytics to help service providers to efficiently and effectively implement a health promotion program to prevent hypertension. First, we developed a prediction model to identify people who are at risk for developing hypertension. Then, to eliminate specific risk factors for each of these individuals, we proposed four methods to create an index that represents the importance of each intervention program, which is a subprogram of the health promotion program. This index can be used to recommend appropriate intervention programs for each individual. We used the national sample cohort database of South Korea to offer a case study of the implementation of the proposed procedure. The constructed prediction model using logistic regression has adequate accuracy, and the proposed index that uses different methods has similar results to those of a doctor. This two-step procedure by automatic modeling based on data will be useful to save human resources and to provide informative and personalized results based on individual healthcare records.

Keywords: prediction; logistic regression; high-risk group; intervention program; importance; index; priority; case study (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:orserv:v:10:y:2018:i:3:p:289-301

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