基于随机森林和Apriori的心理问题预测技术研究

Research on psychological problem prediction technology based on random forest and Apriori

针对目前国内外对心理问题进行智能化分析的研究较少,且存在算法效率、准确率较低等问题,文中提出了基于随机森林数据挖掘方法的心理问题预测方法。该方法对数据库中的原始心理数据进行选择与预处理,利用随机森林和Apriori数据挖掘算法对预处理后的数据进行深层知识的挖掘,并通过可视化界面进行挖掘结果展示。三项实验测试结果表明,所提方法的各功能模块均能按照设计需求较好地实现对应功能,并能够在多种测试环境下稳定运行。同时,相对于其他对比方法,其对于心理问题预测具有较高的准确率,且平均准确率能达到88.74%。

At present,there are few researches on intelligent analysis of psychological problems at home and abroad,and there are some problems such as low algorithm efficiency and accuracy.This paper proposes a psychological problem prediction method based on random forest data mining method.This method selects and preprocesses the original psychological data in the database,then uses random forest and Apriori data mining algorithm to mine the deep knowledge of the preprocessed data,and displays the mining results through the visual interface.The results of three experiments show that each functional module of the proposed method can better realize the corresponding functions according to the design requirements,and can run stably in a variety of test environments.At the same time,compared with other comparison methods,it has a high accuracy in predicting psychological problems,and the average accuracy can reach 88.74%.