Early Detection of College Students' Psychological Problems Based on Decision Tree Model

The paper starts with the research on the early discovery of college students' psychological problems. Besides, it analyzes the data of the general survey of college students' mental health in a certain university, the existing data of students with psychological problems, and the question...

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Autores principales: Huang, Yunpeng, Li, Shaoan, Lin, Bo, Ma, Shuai, Guo, Jian, Wang, Chunli
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Psychology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9400831/
https://www.ncbi.nlm.nih.gov/pubmed/36033043
http://dx.doi.org/10.3389/fpsyg.2022.946998
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author Huang, Yunpeng
Li, Shaoan
Lin, Bo
Ma, Shuai
Guo, Jian
Wang, Chunli
author_facet Huang, Yunpeng
Li, Shaoan
Lin, Bo
Ma, Shuai
Guo, Jian
Wang, Chunli
author_sort Huang, Yunpeng
collection PubMed
description The paper starts with the research on the early discovery of college students' psychological problems. Besides, it analyzes the data of the general survey of college students' mental health in a certain university, the existing data of students with psychological problems, and the questionnaire data of students' basic information in school. By comprehensively using the decision tree model and Kendall correlation analysis and other methods, using Python and SPSS software to preprocess the data and realize the model, it can obtain a psychological problem prediction model based on the objective behavior data of college students. The model is actually analyzed, and it gets good results.
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spelling pubmed-94008312022-08-25 Early Detection of College Students' Psychological Problems Based on Decision Tree Model Huang, Yunpeng Li, Shaoan Lin, Bo Ma, Shuai Guo, Jian Wang, Chunli Front Psychol Psychology The paper starts with the research on the early discovery of college students' psychological problems. Besides, it analyzes the data of the general survey of college students' mental health in a certain university, the existing data of students with psychological problems, and the questionnaire data of students' basic information in school. By comprehensively using the decision tree model and Kendall correlation analysis and other methods, using Python and SPSS software to preprocess the data and realize the model, it can obtain a psychological problem prediction model based on the objective behavior data of college students. The model is actually analyzed, and it gets good results. Frontiers Media S.A. 2022-08-10 /pmc/articles/PMC9400831/ /pubmed/36033043 http://dx.doi.org/10.3389/fpsyg.2022.946998 Text en Copyright © 2022 Huang, Li, Lin, Ma, Guo and Wang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychology
Huang, Yunpeng
Li, Shaoan
Lin, Bo
Ma, Shuai
Guo, Jian
Wang, Chunli
Early Detection of College Students' Psychological Problems Based on Decision Tree Model
title Early Detection of College Students' Psychological Problems Based on Decision Tree Model
title_full Early Detection of College Students' Psychological Problems Based on Decision Tree Model
title_fullStr Early Detection of College Students' Psychological Problems Based on Decision Tree Model
title_full_unstemmed Early Detection of College Students' Psychological Problems Based on Decision Tree Model
title_short Early Detection of College Students' Psychological Problems Based on Decision Tree Model
title_sort early detection of college students' psychological problems based on decision tree model
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9400831/
https://www.ncbi.nlm.nih.gov/pubmed/36033043
http://dx.doi.org/10.3389/fpsyg.2022.946998
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