[Objective] This empirical study tries to identify risk factors for diseases from the heterogeneous Electronic Medical Records (EMR). [Methods] First, we collected EMR with various data structures. Second, we built models to predict risk factors for diseases with the help of three algorithms (i.e., decision-making tree, logistic regression and neutral network). Finally, we compared and evaluated these models statistically. [Results] The Decision Tree Model achieved higher recall and precision rates than the Logistic Regression and Neural Network ones. However, there was no significant difference among them. [Limitations] We did not optimize the EMR’s properties. [Conclusions] The Decision Tree Model does a better job than the Logistic Regression and Neural Network models in discovering the risk factors to predict diseases. The framework of knowledge discovery based on data mining algorithms, provides some directions for future research.
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Mu Dongmei,Ren Ke. Discovering Knowledge from Electronic Medical Records with Three Data Mining Algorithms. New Technology of Library and Information Service, 2016, 32(6): 102-109.
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