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KMID : 1129720160330030102
Korean Journal of Acupuncture
2016 Volume.33 No. 3 p.102 ~ p.113
Machine Learning Approach to Blood Stasis Pattern Identification Based on Self-reported Symptoms
Kim Hyun-Ho

Yang Seung-Bum
Kang Yeon-Seok
Park Young-Bae
Kim Jae-Hyo
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.
KEYWORD
blood stasis, pattern identification, machine learning, logistic regression, decision tree
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