Comparison of Three Data Mining Algorithms in Knowledge Discovery of Electronic Medical Records postprint

Abstract: 【Objective】Disease risk factors were discovered from heterogeneous electronic medical record data to provide reference for data mining and knowledge discovery. 【Method】Clinical electronic medical record data with various structures were selected, and three data mining algorithms, decision tree, logistic regression and neural network, were used to establish disease risk factor prediction models, and the three prediction models were compared and analyzed statistically. . [Results] The precision and recall of the decision tree prediction model are higher than those of logistic regression and neural network, and the overall performance of the decision tree is the best, but there is little difference between the three. [Limitations] The attributes of electronic medical records are not optimized. 【Conclusion】Decision tree is superior to logistic regression and neural network in the discovery of risk factors and prediction of disease. In the research, a knowledge discovery framework of heterogeneous data sources based on data mining algorithm is established, which provides certain reference and reference for the future domain knowledge discovery and knowledge base construction and the selection of data mining algorithms.