神经网络在大学生心理健康预测中的应用研究

Sci. Program. Pub Date : 2022-01-10 DOI:10.1155/2022/5759239
Haoyue Liu, Jilin Xu
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引用次数: 2

摘要

学生健康的心理状态对获得高质量的教育起着重要作用。因此,对大学生心理健康状况的预测研究具有十分重要的意义,并被认为是一个研究热点。在这个具体的研究中,采用BP算法从学生的历史数据中学习不同学生特征的真实性,包括:心理特征、个人基本特征和社会经济特征。在建模的初始阶段,数据预处理步骤用于准备BP算法用于建模的数据。使用BP算法的基本原理是其处理数据异质性和探索不同特征之间相关性的能力。该模型增强了BP算法对学生心理问题风险预测的能力,实现了更高的心理问题预测精度。结果表明,预测值与实测值的误差为0.88%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on the Use of Neural Network for the Prediction of College Students' Mental Health
A healthy mental status of students plays an important role in getting quality of education. Hence, research on the prediction of college students’ mental health status is of great importance and considered as a hot area of research. In this specific research study, back propagation (BP) algorithm is adopted to learn verities of characteristics of different students from the historical data of the students including: psychological characteristics, basic personal characteristics, and socio-economic characteristics. In the initial stage of the modeling, data preprocessing steps are used to prepare the data to be used by the BP algorithm for building model. The rationales behind the use of BP algorithm are its capability of handling heterogeneity of data and exploring correlations among different characteristics. The proposed model enhances the capability of BP algorithm for risk prediction of psychological problem of the students and achieves higher precision of psychological problem prediction. The results obtained show that the error between the predicted and measured values is 0.88%.
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