A systematic literature review and classification of knowledge discovery in traditional medicine.

Highlights • It is the first comprehensive SLR in the use of DM methods in traditional medicine. • The main application domain of machine learning techniques in TM were identified. • BNs, ANNs and SVMs, were recognized as being the methods frequently used in TM. • Most of selected papers applied single scaler methods for performance evaluation. Abstract Introduction and Objective Despite the importance of machine learning methods application in traditional medicine there is a no systematic literature review and a classification for this field. This is the first comprehensive literature review of the application of data mining methods in traditional medicine. Method We reviewed 5 database between 2000 to 2017 based on the Kitchenham systematic review methodology. 502 articles were identified and reviewed for their relevance to application of machine learning methods in traditional medicine, 42 selected papers were classified and categorized on four dimension; 1) application domain of data mining techniques in traditional medicine; 2) the data mining methods most frequently used in traditional medicine; 3) main strength and limitation of data mining techniques in traditional medicine; 4) the performance evaluation methods in data mining methods in traditional medicine. Result The result obtained showed that main application domain of data mining techniques in traditional medicine was related to syndrome differentiation. Bayesian Networks (BNs), Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) were recognized as being the methods most frequently applied in traditional medicine. Furthermore, each data mining techniques has its own strength and limitations when applied in traditional medicine. Single scaler methods were frequently used for performance evaluation of data mining methods. Conclusion Machine learning methods have become an important research field in traditional medicine. Our research provides information about this methods by examining the related articles. [ABSTRACT FROM AUTHOR]