基于人工智能和随机森林的心理健康分析方法研究

Design of mental health analysis method based on artificial intelligence and random forest

针对传统心理健康判断方法无法量化患者的心理健康程度,严重依赖心理医生和心理健康咨询师判断的问题,文中着眼于大学生心理健康情况,分别从学生的生理和心理两个角度进行心理健康分析。依靠智能可穿戴设备以及社交软件平台,展开了基于人工智能和随机森林的心理健康分析方法研究。该方法将心率、运动行为、环境以及社交平台的文本信息作为心理健康分析的原始数据;原始数据经过预处理后,通过人工智能中的卷积神经网络进行各类信息的特征提取;使用随机森林算法作为分类器来判断用户的心理健康程度。测试与数据分析结果表明,文中所述方案具有80.4%的识别准确率,比支持向量机、前馈神经网络算法的准确率更高,证明了该方案的有效性与可行性。

In view of the fact that traditional mental health judgment methods cannot quantify the mental health of patients,and rely heavily on the judgments of psychologists and mental health consultants,this article focuses on the mental health of college students and analyzes mental health from the perspectives of students'physiology and psychology.Relying on smart wearable devices and social software platforms,researches on mental health analysis methods based on artificial intelligence and random forest were conducted.Heart rate,sports behavior,environment and text information on social platforms are used as raw data for mental health analysis;After preprocessing the original data,the convolutional neural network in artificial intelligence is used to extract the characteristics of various types of information;The random forest algorithm is used as a classifier to judge the user's mental health.After testing,the scheme described in this paper has a recognition accuracy rate of 80.4%,which is higher than the support vector machine and feedforward neural network algorithms.This shows the effectiveness and feasibility of the scheme described in this paper.