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http://dx.doi.org/10.14406/acu.2016.011

Machine Learning Approach to Blood Stasis Pattern Identification Based on Self-reported Symptoms  

Kim, Hyunho (Department of Biofunctional Medicine & Diagnostics, College of Korean Medicine, Kyung Hee University)
Yang, Seung-Bum (Department of Medical Non-commissioned Officer, Wonkwang Health Science)
Kang, Yeonseok (Department of Medical History, College of Korean Medicine, Wonkwang University)
Park, Young-Bae (Department of Biofunctional Medicine & Diagnostics, College of Korean Medicine, Kyung Hee University)
Kim, Jae-Hyo (Department of Meridian & Acupoint, College of Korean Medicine, Wonkwang University)
Publication Information
Korean Journal of Acupuncture / v.33, no.3, 2016 , pp. 102-113 More about this Journal
Abstract
Objectives : This study is aimed at developing and discussing the prediction model of blood stasis pattern of traditional Korean medicine(TKM) using machine learning algorithms: multiple logistic regression and decision tree model. Methods : First, we reviewed the blood stasis(BS) questionnaires of Korean, Chinese, and Japanese version to make a integrated BS questionnaire of patient-reported outcomes. Through a human subject research, patients-reported BS symptoms data were acquired. Next, experts decisions of 5 Korean medicine doctor were also acquired, and supervised learning models were developed using multiple logistic regression and decision tree. Results : Integrated BS questionnaire with 24 items was developed. Multiple logistic regression models with accuracy of 0.92(male) and 0.95(female) validated by 10-folds cross-validation were constructed. By decision tree modeling methods, male model with 8 decision node and female model with 6 decision node were made. In the both models, symptoms of 'recent physical trauma', 'chest pain', 'numbness', and 'menstrual disorder(female only)' were considered as important factors. Conclusions : Because machine learning, especially supervised learning, can reveal and suggest important or essential factors among the very various symptoms making up a pattern identification, it can be a very useful tool in researching diagnostics of TKM. With a proper patient-reported outcomes or well-structured database, it can also be applied to a pre-screening solutions of healthcare system in Mibyoung stage.
Keywords
blood stasis; pattern identification; machine learning; logistic regression; decision tree;
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Times Cited By KSCI : 11  (Citation Analysis)
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1 Yang DH, Park YJ, Park YB, Lee SC. Development of questionnaires for blood stasis pattern. J Korean Institude of Oriental Medical Diagnostics. 2006 ; 10(1) : 141-52.
2 Kim JB, Choi SH, Ahn KS. Study on the effects of Taorenchengqitang and its components on blood stasis model. Korean J Oriental Medical Pathology. 1997 ; 11(1) : 65-76.
3 Gorsuch. Factor Analysis, 2nd edition. NJ : Lawrence Erlbaum. 1983.
4 Breiman L, Friedman JH, Olshen RA, Stone CJ. Classification and regression trees. Monterey, CA : Wadsworth & Brooks/Cole Advanced Books & Software. 1984.
5 Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977 ; 33 : 159-74.   DOI
6 Mary LM. Interrater reliability: the kappa statistic. Biochem Med. 2012 ; 22(3) : 276-82.
7 Yim HJ, Kim SH, Lee SR, Jung IN. Study to develop the instrument of pattern identification for Hwa-byung. Korean J Oriental Physiology & Pathology. 2008 ; 22(5) : 1071-7.
8 Park YJ, Park JS, Kim MY, Park YB. Development of a valid and reliable phlegm pattern questionnaire. J Altern Complement Med. 2011 ; 17(9) : 851-8.   DOI
9 Park YJ, Lim JS, Park YB. Development of a valid and reliable food retention questionnaire. European Journal of Integrative Medicine. 2013 ; 5(5) : 432-7.   DOI
10 Yoon KJ, Park YB, Park YJ, Kim MY. Development and validation of a Lao Juan questionnaire. Chin J Intergr Med. 2015 ; 21(7) : 500-6.   DOI
11 Ryu HH, Lee H, Kim H, Kim JY. Reliability and validity of a cold-heat pattern questionnaire for traditional Chinese medicine. J Altern Complement Med. 2010 ; 16(6) : 663-7.   DOI
12 Park YJ, Yang DH, Lee JM, Park YB. Development of a valid and reliable blood stasis questionnaire and its relationship to heart rate variability. Complement Ther Med. 2013 ; 21(6) : 633-40.   DOI
13 Kim SH, Ko BH, Song IB. A study on the standardization of QSCC II. J of Sasang Constitutional Medicine. 1996 ; 8(1) : 187-246.
14 Lee JH, Ko BH, Song IB. A study on the validation of QSCC II. J of Sasang Constitutional Medicine. 1996 ; 8(1) : 247-94.
15 Park DM, Lee SR, Kang WC, Jung IC. Preliminary Study to Develop the Instrument of Pattern Identification for Jing Ji and Zheng Chong. Journal of Oriental Neuropsychiatry. 2010 ; 21(2) : 1-15.
16 Kim KK, Kim JW, Lee EJ, Kim JY, Choi SM. Study on classification function into Sasang constitution using data mining techniques. Korean J Oriental Physiology & Pathology. 2004 ; 18(6) : 1938-44.
17 Lee IS, Bae GM. A Clinical Study on Differentiation of Syndromes of Amenorrhea or Oligomenorrhea with DSOM. The Journal of Oriental Obstetrics and Gynecology. 2009 ; 22(2) : 189-208.
18 Shin JH, Jung WY, Moon YK, Nam HJ, Kim YB, Lee JH, et al. An Expert Survey for Developing the Pattern Diagnosis Instrument of Acne. 2015 ; 28(2) : 23-32.
19 Kim SK, Jang HC, Kim JH, Kim C, Yea SJ, Song MY. Computational methods for traditional Korean medicine : A survey. Korean J Oriental Physiology & Pathology. 2011 ; 25(5) : 894-9.
20 Hong JW, Kim YI, Park SJ, Kim BC, Eom IK, Hwang MW, et al. Data mining algorithms for the development of Sasang type diagnosis. Korean J Oriental Physiology & Pathology. 2009 ; 23(6) : 1234-40.
21 Chae H, Hwang SM, Eom IK, Kim BC, Kim YI, Kim BJ, et al. Development of Sasang type diagnostic test with neural network. Korean J Oriental Physiology & Pathology. 2009 ; 23(4) : 765-71.
22 Shin YS, Park YB, Park YJ, Kim MY, Lee SC, Oh HS. A fundamental study for 8 constitution medicine diagnosis expert system development. J Korean Institude of Oriental Medical Diagnostics. 2007 ; 11(1) : 25-47.
23 Shin YS, Park YB, Park YJ, Kim MY, Lee SC, Oh HS. A study for 8 constitution medicine diagnosis expert system development. J Korean Institude of Oriental Medical Diagnostics. 2008 ; 12(1) : 142-84.
24 Hand D, Mannila H, Smyth P. Principles of Data Mining. Cambridge, MA : MIT Press. 2001.