DOI QR코드

DOI QR Code

A method for evaluating and scoring of health status

건강수준의 측정 및 평점화 모형의 설계

  • Oh, Piljae (Department of Statistics and Actuarial Science, Soongsil University) ;
  • Kim, Hyeoncheol (Samjong KPMG Digital Consulting) ;
  • Kwon, Hyuksung (Department of Statistics and Actuarial Science, Soongsil University)
  • 오필재 (숭실대학교 정보통계.보험수리학과) ;
  • 김현철 (삼정 KPMG Digital 본부) ;
  • 권혁성 (숭실대학교 정보통계.보험수리학과)
  • Received : 2020.01.07
  • Accepted : 2020.03.23
  • Published : 2020.06.30

Abstract

Health is an important issue due to increased life expectancy. As a result, the demand for industry and services associated with individual health, health-related programs and services will be facilitated by a method to evaluate and classify the health level of an individual based on various factors. This study suggests a methodology to measure and score an individual health level. A credit scoring model was introduced to implement the categorization of variables, construct a prediction model, and to score individual health level. Cohort DB provided by National Health Insurance Service was used to illustrate overall procedures. It is expected that the suggested model can be utilized in designing and managing health care services as well as other health-related programs.

최근 기대수명의 증가로 건강에 대한 관심이 늘어나고 있으며 이에 따라 건강관련 산업 및 서비스에 대한 수요도 증가하고 있다. 개인의 건강상태를 다양한 요소들을 이용하여 평가하고 분류할 수 있는 방법을 통해 다양한 건강관련 프로그램 및 서비스를 보다 합리적으로 운영할 수 있을 것이다. 본 연구에서는 기존 연구를 통해 잘 알려진 건강상태 관련 요인들을 이용하여 건강수준을 측정하고 평점화하는 방안을 제시하였다. 이를 위해 신용평가모형의 변수 선정과 범주화, 모형 도출, 평점화로 이어지는 일련의 과정에서 사용하는 방법론을 도입하였고 모형의 적합을 위해서 국민건강보험공단에서 제공하는 표본 코호트 DB를 이용하였다. 본 연구에서 도출된 건강수준 평가모형은 헬스케어 및 건강관련 서비스에 대한 구조 설계 및 운영에 적절하게 활용될 수 있을 것으로 기대된다.

Keywords

References

  1. Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy, The Journal of Finance, 23, 589-609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x
  2. Bae, C. Y., Kang, Y. G., Kim, S., et al. (2008). Development of models for predicting biological age (BA) with physical, biochemical, and hormonal parameters, Archives of Gerontology and Geriatrics, 47, 253-265. https://doi.org/10.1016/j.archger.2007.08.009
  3. Choi, J., Jang, J., An, Y., and Park, S. K. (2018). Blood pressure and the risk of death from noncardiovascular diseases: a population-based cohort study of Korean adults, Journal of Preventive Medicine and Public Health, 51, 298-309. https://doi.org/10.3961/jpmph.18.212
  4. Durand, D. (1941). Risk Elements in Consumer Installment Financing (Technical Ed), National Bureau of Economic Research, New York.
  5. Evans, M., Roberts, A., Davies, S., and Rees, A. (2004). Medical lipid-regulating therapy, Drugs, 64, 1181-1196. https://doi.org/10.2165/00003495-200464110-00003
  6. Finlay, S. (2012). Credit Scoring, Response Modeling, and Insurance Rating: A Practical Guide to Forecasting Consumer Behavior, Palgrave Macmillan, New York.
  7. Furukawa, T., Inoue, M., Kajiya, F., Inada, H., Takasugi, S., Fukui, S., Takeda, H. and Abe, H. (1975). Assessment of biological age by multiple regression analysis, Journal of Gerontology, 30, 422-434. https://doi.org/10.1093/geronj/30.4.422
  8. Goggins, W. B., Woo, J., Sham, A., and Ho, S. C. (2005). Frailty index as a measure of biological age in a Chinese population, The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 60, 1046-1051. https://doi.org/10.1093/gerona/60.8.1046
  9. Hamer, M. M. (1983). Failure prediction: sensitivity of classification accuracy to alternative statistical methods and variable sets, Journal of Accounting and Public Policy, 2, 289-307. https://doi.org/10.1016/0278-4254(83)90032-7
  10. Hand, D. J. (2009). Measuring classifier performance: a coherent alternative to the area under the ROC curve, Machine learning, 77, 103-123. https://doi.org/10.1007/s10994-009-5119-5
  11. Hernaez, R., Yeh, H. C., Lazo, M., Chung, H. M., Hamilton, J. P., Koteish, A., Potter, J. J., Brancati, F. L., and Clark, J. M. (2013). Elevated ALT and GGT predict all-cause mortality and hepatocellular carcinoma in Taiwanese male: a case-cohort study, Hepatology international, 7, 1040-1049. https://doi.org/10.1007/s12072-013-9476-6
  12. Hong, C. S. and Park, Y. S. (2005). Efficiency comparison of statistical credit evaluation models, Research Institute of Applied Statistics Sungkyunkwan University, 13, 93-107.
  13. Huang, Z., Chen, H., Hsu, C. J., Chen, W. H., and Wu, S. (2004). Credit rating analysis with support vector machines and neural networks: a market comparative study, Decision Support Systems, 37, 543-558. https://doi.org/10.1016/S0167-9236(03)00086-1
  14. Irie, F., Iso, H., Sairenchi, T., et al. (2006). The relationships of proteinuria, serum creatinine, glomerular filtration rate with cardiovascular disease mortality in Japanese general population, Kidney International, 69, 1264-1271. https://doi.org/10.1038/sj.ki.5000284
  15. Jeon, H. G., Won, J. Y., Peng, X., and Lee, K. C. (2019). Investigating effects of emotional states on the glucose control of diabetes in Korean adults, Journal of Digital Convergence, 17, 301-311. https://doi.org/10.14400/JDC.2019.17.1.301
  16. Jeon, W. J. and Seo, Y. W. (2018). Analysis of important indicators of TCB using GBM, Journal of Society for e-Business Studies, 22, 159-173. https://doi.org/10.7838/jsebs.2017.22.4.159
  17. Kang, Y. G., Suh, E., Lee, J. W., Kim, D. W., Cho, K. H., and Bae, C. Y. (2018). Biological age as a health index for mortality and major age-related disease incidence in Koreans: National Health Insurance Service - Health Screening 11-year follow-up study, Clinical Interventions in Aging, 13, 429-436. https://doi.org/10.2147/CIA.S157014
  18. Katzmarzyk, P. T., Reeder, B. A., Elliott, S., Joffres, M. R., Pahwa, P., Raine, K. D., Kirkland S. A., and Paradis, G. (2012). Body mass index and risk of cardiovascular disease, cancer and all-cause mortality, Canadian Journal of Public Health, 103, 147-151. https://doi.org/10.1007/BF03404221
  19. Kim, J. Y., Jang, W. J., and Gim, G. Y. (2019). Development of a personal credit scoring model (COMMERCE Score) using on-line commerce data, Journal of Information Technology and Architecture, 16, 45-55. https://doi.org/10.22865/JITA.2019.16.1.45
  20. Klemera, P. and Doubal, S. (2006). A new approach to the concept and computation of biological age, Mechanisms of Ageing and Development, 127, 240-248. https://doi.org/10.1016/j.mad.2005.10.004
  21. Lee, J. Y., Kim, K. H., and Lee, J. S. (2013). Construction of Sample Database from National Health Information Database. Seminar on Application of National Health Information Bigdata.
  22. Martin, M. J., Browner, W. S., Hulley, S. B., Kuller, L. H., and Wentworth, D. (1986). Serum cholesterol, blood pressure, and mortality: implications from a cohort of 361,662 men, The Lancet, 2(8513), 933-936.
  23. Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy, Journal of Accounting Research, 18, 109-131. https://doi.org/10.2307/2490395
  24. Park, C. S. and Kim, M. S. (2011). Credit evaluation model for medical venture business by the analytic hierarchy process, Asia-Pacific Journal of Business Venturing and Entrepreneurship, 6, 133-147.
  25. Park, J., Cho, B., Kwon, H., and Lee, C. (2009). Developing a biological age assessment equation using principal component analysis and clinical biomarkers of aging in Korean men, Archives of Gerontology and Geriatrics, 49, 7-12. https://doi.org/10.1016/j.archger.2008.04.003
  26. Pierleoni, P., Belli, A., Concetti, R., Palma, L., Pinti, F., Raggiunto, S., Sabbatini, L., Valenti, S., and Monteriu, A. (2019). Biological age estimation using an eHealth system based on wearable sensors, Journal of Ambient Intelligence and Humanized Computing, 1-12.
  27. Stocks, T., Van Hemelrijck, M. V., Manjer, J., et al. (2012). Blood pressure and risk of cancer incidence and mortality in the Metabolic Syndrome and Cancer Project, Hypertension, 59, 802-810. https://doi.org/10.1161/HYPERTENSIONAHA.111.189258
  28. Wilson, P. W., Abbott, R. D., and Castelli, W. P. (1988). High density lipoprotein cholesterol and mortality. The Framingham Heart Study, Arteriosclerosis, 8, 737-741. https://doi.org/10.1161/01.ATV.8.6.737
  29. Woo, H. S., Lee, S. H., and Cho, H. J. (2013). Building credit scoring models with various types of target variables, Journal of the Korean Data and Information Science Society, 24, 85-94. https://doi.org/10.7465/jkdi.2013.24.1.85
  30. Yi, S. W., Park, S., Lee, Y. H., Park, H. J., Balkau, B., and Yi, J. J. (2017). Association between fasting glucose and all-cause mortality according to sex and age: a prospective cohort study, Scientific Reports, 7, 1-9. https://doi.org/10.1038/s41598-016-0028-x
  31. Yoo, J., Kim, Y., Cho, E. R., and Jee, S. H. (2017). Biological age as a useful index to predict seventeen-year survival and mortality in Koreans, BMC Geriatrics, 17, 7. https://doi.org/10.1186/s12877-016-0407-y