目的:本文应用基于数据挖掘流程的logistic回归方法建模,对亚健康状态的人群进行分类并分析其临床特征。方法:针对亚健康状态流行病学调查数据进行统计分析,采用从数据理解、数据准备、变量筛选和选择logistic回归建模的数据挖掘流程的方法,确定最终的回归方程,从而得到亚健康状态的判别方程及其临床特征。结果:建立了两种logistie回归模型,并在此基础上应用数据挖掘的思想对回归方程做进一步的测试,得到了分类准确率较高的验证,其结果提示亚健康的主要临床特征表现为躯体的疲劳、睡眠不实、记忆力和工作效率下降、饮食二便失调、心理的空虚感和情绪易怒等。结论:在分析判断和解释影响因素较复杂、自变量较多的亚健康人群临床特征研究中,使用传统意义的logistic回归建模具有很大的优越性。

Objective: this paper aims to analyze the survey data using logistic regression method based on data mining process to get the final classification and the clinic characteristic of the sub-health crowd. Method: the sub-health epidemiological data is analyzed firstly by the whole data understanding and then by selecting variables and finally by choosing the appropriate model. Thus, the classification equation and the clinic characteristic of sub -health are obtained. Results: Two logistic regression models are established in two ways, each of which is also tested using testing data set to reach the classification accuracy. And the results are satisfying which show that the main clinic characteristics are body fatigue, sleep difficulty, bad memory, work efficiency declining, mental blankness, irascibility, etc. Conclusion: This method is superior to the traditional logistic regression method in dealing with the case with many explanation variables, showing great advantage.

北京市科委中医药科研基金资助项目(H010910160119)亚健康状态中医基本证候流行病学调查．