In today’s society, the development of information technology is very rapid, and the transmission and sharing of information has become a development trend. The results of data analysis and research are gradually applied to various fields of social development, structured analysis, and research. Data mining of electronic medical records in the medical field is gradually valued by researchers and has become a major work in the medical field. In the course of clinical treatment, electronic medical records are edited, including all personal health and treatment information. This paper mainly introduces the research of diabetes risk data mining method based on electronic medical record analysis and intends to provide some ideas and directions for the research of diabetes risk data mining method. This paper proposes a research strategy of diabetes risk data mining method based on electronic medical record analysis, including data mining and classification rule mining based on electronic medical record analysis, which are used in the research experiment of diabetes risk data mining method based on electronic medical record analysis. The experimental results in this paper show that the average prediction accuracy of the decision tree is 91.21%, and the results of the training set and the test set are similar, indicating that there is no overfitting of the training set.

中文翻译：

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基于电子病历分析的糖尿病风险数据挖掘方法

当今社会，信息技术的发展非常迅速，信息的传递和共享已经成为一种发展趋势。数据分析和研究的成果逐渐应用于社会发展、结构化分析和研究的各个领域。医学领域的电​​子病历数据挖掘逐渐受到研究人员的重视，成为医学领域的一项主要工作。在临床治疗过程中，编辑电子病历，包括所有个人健康和治疗信息。本文主要介绍基于电子病历分析的糖尿病风险数据挖掘方法研究，旨在为糖尿病风险数据挖掘方法的研究提供一些思路和方向。本文提出了一种基于电子病历分析的糖尿病风险数据挖掘方法的研究策略，包括基于电子病历分析的数据挖掘和分类规则挖掘，用于基于电子病历的糖尿病风险数据挖掘方法的研究实验。记录分析。本文实验结果表明，决策树的平均预测准确率为91.21%，训练集和测试集结果相似，说明训练集不存在过拟合。用于基于电子病历分析的糖尿病风险数据挖掘方法的研究实验。本文实验结果表明，决策树的平均预测准确率为91.21%，训练集和测试集结果相似，说明训练集不存在过拟合。用于基于电子病历分析的糖尿病风险数据挖掘方法的研究实验。本文实验结果表明，决策树的平均预测准确率为91.21%，训练集和测试集结果相似，说明训练集不存在过拟合。