Construction and Optimization of Mental Health Education Consultation Management System Based on Decision Tree Association Rule Mining

This paper studies association rule mining and decision tree algorithm, focusing on the extended research of association rule mining, including the number of generated rules, mining association rules of long itemsets with low support, attribute selection criteria and multivalue attributes in decisio...

Full description

Saved in:
Bibliographic Details
Published in:Mathematical problems in engineering 2022, Vol.2022, p.1-11
Main Authors: Liu, Haoxian, Chen, Xiuyuan
Format: Article
Language:English
Subjects:
Algorithms
Big Data
Classification
College students
Colleges & universities
Correlation coefficients
Data analysis
Data mining
Decision analysis
Decision making
Decision trees
Design optimization
Education
Efficiency
Engineering
Evaluation
Health education
Information technology
Mental disorders
Mental health
Mental health care
Nodes
Parameters
Regression models
Similarity
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c2521-29e1993f621869a9bc727e2b339ae5b1886fa347785588a70b27181a17fa3dab3
cites cdi_FETCH-LOGICAL-c2521-29e1993f621869a9bc727e2b339ae5b1886fa347785588a70b27181a17fa3dab3
container_end_page 11
container_issue
container_start_page 1
container_title Mathematical problems in engineering
container_volume 2022
creator Liu, Haoxian
Chen, Xiuyuan
description This paper studies association rule mining and decision tree algorithm, focusing on the extended research of association rule mining, including the number of generated rules, mining association rules of long itemsets with low support, attribute selection criteria and multivalue attributes in decision tree algorithm. This paper conducts in-depth research and analysis on the design and optimization of the mental health education consultation management system using the association rule decision tree algorithm. This paper analyzes the meaning of parameters under the support-confidence-interest model, and uses regression method to design equations between the number of rules and parameters. We use the multiple correlation coefficient to test the fitting effect of the equation, and use the significance test to verify whether the coefficient of the parameter is significantly zero. On the one hand, the widely used psychological crisis prevention measures generally include the screening of the SCL psychological scale in the early stage of first-year enrolment, the holding of general psychological knowledge lectures and courses, and the opening of psychological counselling rooms with a low penetration rate, but these practices are to a certain extent. In other words, it cannot enable the student administrator to grasp the psychological status of the students in a timely, effective, and dynamic manner, to timely intervene in the possible crisis. Not only the number of attribute values of the current node is considered but also the size of the variable precision clear area of the lower node is considered, that is, the two-layer nodes of the tree are considered at the same time. The new attribute selection method not only overcomes the shortcomings of the original algorithm, but also has the advantages of variable precision rough sets. This paper uses a new criterion for attribute selection, weighted roughness and complexity, which comprehensively considers the classification accuracy and the number of branches. In order to reduce the influence of noisy data and missing values, the algorithm uses a class prediction method based on matching degree. Through comparative experiments, the effectiveness of the method proposed in this paper is verified. We propose a new calculation formula for the similarity of the child nodes of the label set to evaluate the effect of attribute classification, and comprehensively consider the situation that the elements in the two multilabe
doi_str_mv 10.1155/2022/7307741
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2648810357</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2648810357</sourcerecordid><originalsourceid>FETCH-LOGICAL-c2521-29e1993f621869a9bc727e2b339ae5b1886fa347785588a70b27181a17fa3dab3</originalsourceid><addsrcrecordid>eNp9kEFOwzAQRS0EEqWw4wCWWEKox45jZ1lKoUitKkGR2EVO4rSuUqfEjlA5AOcmIV2zmpn_38xIH6FrIPcAnI8ooXQkGBEihBM0AB6xgEMoTtue0DAAyj7O0YVzW0IocJAD9DOprPN1k3lTWaxsjpd7b3bmW_0JVYEX2npV4plWpd_gad5kvdUtNqXvh4Wyaq13LYrfDs7rHX5QTue4tR51ZlzHrGqt8di5KjP90mtTarww1tj1JTorVOn01bEO0fvTdDWZBfPl88tkPA8yyikENNYQx6yIKMgoVnGaCSo0TRmLleYpSBkVioVCSM6lVIKkVIAEBaKVc5WyIbrp7-7r6rPRzifbqqlt-zKhUSglEMZFS931VFZXztW6SPa12an6kABJuqSTLunkmHSL3_b4xthcfZn_6V-oJX47</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2648810357</pqid></control><display><type>article</type><title>Construction and Optimization of Mental Health Education Consultation Management System Based on Decision Tree Association Rule Mining</title><source>Wiley-Blackwell Titles (Open access)</source><source>Publicly Available Content Database</source><source>Coronavirus Research Database</source><creator>Liu, Haoxian ; Chen, Xiuyuan</creator><contributor>Sun, Gengxin ; Gengxin Sun</contributor><creatorcontrib>Liu, Haoxian ; Chen, Xiuyuan ; Sun, Gengxin ; Gengxin Sun</creatorcontrib><description>This paper studies association rule mining and decision tree algorithm, focusing on the extended research of association rule mining, including the number of generated rules, mining association rules of long itemsets with low support, attribute selection criteria and multivalue attributes in decision tree algorithm. This paper conducts in-depth research and analysis on the design and optimization of the mental health education consultation management system using the association rule decision tree algorithm. This paper analyzes the meaning of parameters under the support-confidence-interest model, and uses regression method to design equations between the number of rules and parameters. We use the multiple correlation coefficient to test the fitting effect of the equation, and use the significance test to verify whether the coefficient of the parameter is significantly zero. On the one hand, the widely used psychological crisis prevention measures generally include the screening of the SCL psychological scale in the early stage of first-year enrolment, the holding of general psychological knowledge lectures and courses, and the opening of psychological counselling rooms with a low penetration rate, but these practices are to a certain extent. In other words, it cannot enable the student administrator to grasp the psychological status of the students in a timely, effective, and dynamic manner, to timely intervene in the possible crisis. Not only the number of attribute values of the current node is considered but also the size of the variable precision clear area of the lower node is considered, that is, the two-layer nodes of the tree are considered at the same time. The new attribute selection method not only overcomes the shortcomings of the original algorithm, but also has the advantages of variable precision rough sets. This paper uses a new criterion for attribute selection, weighted roughness and complexity, which comprehensively considers the classification accuracy and the number of branches. In order to reduce the influence of noisy data and missing values, the algorithm uses a class prediction method based on matching degree. Through comparative experiments, the effectiveness of the method proposed in this paper is verified. We propose a new calculation formula for the similarity of the child nodes of the label set to evaluate the effect of attribute classification, and comprehensively consider the situation that the elements in the two multilabel sets appear or not appear at the same time, so that the calculation of the similarity of the label set is more comprehensive. The experimental results show that the model proposed in this paper can excavate the dialectical combination of multiple factors. By comparing with the existing algorithms, the classification effect of the proposed algorithm is verified. The classification algorithm proposed in this paper is more suitable for dealing with multi-value attributes and multiclass data classification problems. The psychological evaluation and counselling system designed in this paper has achieved the expected goal. The results of this paper can improve the problems existing in the work of psychological counselling services.</description><identifier>ISSN: 1024-123X</identifier><identifier>EISSN: 1563-5147</identifier><identifier>DOI: 10.1155/2022/7307741</identifier><language>eng</language><publisher>New York: Hindawi</publisher><subject>Algorithms ; Big Data ; Classification ; College students ; Colleges &amp; universities ; Correlation coefficients ; Data analysis ; Data mining ; Decision analysis ; Decision making ; Decision trees ; Design optimization ; Education ; Efficiency ; Engineering ; Evaluation ; Health education ; Information technology ; Mental disorders ; Mental health ; Mental health care ; Nodes ; Parameters ; Regression models ; Similarity</subject><ispartof>Mathematical problems in engineering, 2022, Vol.2022, p.1-11</ispartof><rights>Copyright © 2022 Haoxian Liu and Xiuyuan Chen.</rights><rights>Copyright © 2022 Haoxian Liu and Xiuyuan Chen. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c2521-29e1993f621869a9bc727e2b339ae5b1886fa347785588a70b27181a17fa3dab3</citedby><cites>FETCH-LOGICAL-c2521-29e1993f621869a9bc727e2b339ae5b1886fa347785588a70b27181a17fa3dab3</cites><orcidid>0000-0003-3824-8944</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2648810357/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2648810357?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>315,783,787,4031,25765,27935,27936,27937,37024,38528,43907,44602,74740,75454</link.rule.ids></links><search><contributor>Sun, Gengxin</contributor><contributor>Gengxin Sun</contributor><creatorcontrib>Liu, Haoxian</creatorcontrib><creatorcontrib>Chen, Xiuyuan</creatorcontrib><title>Construction and Optimization of Mental Health Education Consultation Management System Based on Decision Tree Association Rule Mining</title><title>Mathematical problems in engineering</title><description>This paper studies association rule mining and decision tree algorithm, focusing on the extended research of association rule mining, including the number of generated rules, mining association rules of long itemsets with low support, attribute selection criteria and multivalue attributes in decision tree algorithm. This paper conducts in-depth research and analysis on the design and optimization of the mental health education consultation management system using the association rule decision tree algorithm. This paper analyzes the meaning of parameters under the support-confidence-interest model, and uses regression method to design equations between the number of rules and parameters. We use the multiple correlation coefficient to test the fitting effect of the equation, and use the significance test to verify whether the coefficient of the parameter is significantly zero. On the one hand, the widely used psychological crisis prevention measures generally include the screening of the SCL psychological scale in the early stage of first-year enrolment, the holding of general psychological knowledge lectures and courses, and the opening of psychological counselling rooms with a low penetration rate, but these practices are to a certain extent. In other words, it cannot enable the student administrator to grasp the psychological status of the students in a timely, effective, and dynamic manner, to timely intervene in the possible crisis. Not only the number of attribute values of the current node is considered but also the size of the variable precision clear area of the lower node is considered, that is, the two-layer nodes of the tree are considered at the same time. The new attribute selection method not only overcomes the shortcomings of the original algorithm, but also has the advantages of variable precision rough sets. This paper uses a new criterion for attribute selection, weighted roughness and complexity, which comprehensively considers the classification accuracy and the number of branches. In order to reduce the influence of noisy data and missing values, the algorithm uses a class prediction method based on matching degree. Through comparative experiments, the effectiveness of the method proposed in this paper is verified. We propose a new calculation formula for the similarity of the child nodes of the label set to evaluate the effect of attribute classification, and comprehensively consider the situation that the elements in the two multilabel sets appear or not appear at the same time, so that the calculation of the similarity of the label set is more comprehensive. The experimental results show that the model proposed in this paper can excavate the dialectical combination of multiple factors. By comparing with the existing algorithms, the classification effect of the proposed algorithm is verified. The classification algorithm proposed in this paper is more suitable for dealing with multi-value attributes and multiclass data classification problems. The psychological evaluation and counselling system designed in this paper has achieved the expected goal. The results of this paper can improve the problems existing in the work of psychological counselling services.</description><subject>Algorithms</subject><subject>Big Data</subject><subject>Classification</subject><subject>College students</subject><subject>Colleges &amp; universities</subject><subject>Correlation coefficients</subject><subject>Data analysis</subject><subject>Data mining</subject><subject>Decision analysis</subject><subject>Decision making</subject><subject>Decision trees</subject><subject>Design optimization</subject><subject>Education</subject><subject>Efficiency</subject><subject>Engineering</subject><subject>Evaluation</subject><subject>Health education</subject><subject>Information technology</subject><subject>Mental disorders</subject><subject>Mental health</subject><subject>Mental health care</subject><subject>Nodes</subject><subject>Parameters</subject><subject>Regression models</subject><subject>Similarity</subject><issn>1024-123X</issn><issn>1563-5147</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>COVID</sourceid><sourceid>PIMPY</sourceid><recordid>eNp9kEFOwzAQRS0EEqWw4wCWWEKox45jZ1lKoUitKkGR2EVO4rSuUqfEjlA5AOcmIV2zmpn_38xIH6FrIPcAnI8ooXQkGBEihBM0AB6xgEMoTtue0DAAyj7O0YVzW0IocJAD9DOprPN1k3lTWaxsjpd7b3bmW_0JVYEX2npV4plWpd_gad5kvdUtNqXvh4Wyaq13LYrfDs7rHX5QTue4tR51ZlzHrGqt8di5KjP90mtTarww1tj1JTorVOn01bEO0fvTdDWZBfPl88tkPA8yyikENNYQx6yIKMgoVnGaCSo0TRmLleYpSBkVioVCSM6lVIKkVIAEBaKVc5WyIbrp7-7r6rPRzifbqqlt-zKhUSglEMZFS931VFZXztW6SPa12an6kABJuqSTLunkmHSL3_b4xthcfZn_6V-oJX47</recordid><startdate>2022</startdate><enddate>2022</enddate><creator>Liu, Haoxian</creator><creator>Chen, Xiuyuan</creator><general>Hindawi</general><general>Hindawi Limited</general><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>COVID</scope><scope>CWDGH</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>KR7</scope><scope>L6V</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><orcidid>https://orcid.org/0000-0003-3824-8944</orcidid></search><sort><creationdate>2022</creationdate><title>Construction and Optimization of Mental Health Education Consultation Management System Based on Decision Tree Association Rule Mining</title><author>Liu, Haoxian ; Chen, Xiuyuan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2521-29e1993f621869a9bc727e2b339ae5b1886fa347785588a70b27181a17fa3dab3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Big Data</topic><topic>Classification</topic><topic>College students</topic><topic>Colleges &amp; universities</topic><topic>Correlation coefficients</topic><topic>Data analysis</topic><topic>Data mining</topic><topic>Decision analysis</topic><topic>Decision making</topic><topic>Decision trees</topic><topic>Design optimization</topic><topic>Education</topic><topic>Efficiency</topic><topic>Engineering</topic><topic>Evaluation</topic><topic>Health education</topic><topic>Information technology</topic><topic>Mental disorders</topic><topic>Mental health</topic><topic>Mental health care</topic><topic>Nodes</topic><topic>Parameters</topic><topic>Regression models</topic><topic>Similarity</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Haoxian</creatorcontrib><creatorcontrib>Chen, Xiuyuan</creatorcontrib><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing</collection><collection>CrossRef</collection><collection>Mechanical &amp; Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Advanced Technologies &amp; Aerospace Database‎ (1962 - current)</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>Coronavirus Research Database</collection><collection>ProQuest Middle East &amp; Africa Database</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection (Proquest) (PQ_SDU_P3)</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Advanced Technologies &amp; Aerospace Database</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><jtitle>Mathematical problems in engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Haoxian</au><au>Chen, Xiuyuan</au><au>Sun, Gengxin</au><au>Gengxin Sun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Construction and Optimization of Mental Health Education Consultation Management System Based on Decision Tree Association Rule Mining</atitle><jtitle>Mathematical problems in engineering</jtitle><date>2022</date><risdate>2022</risdate><volume>2022</volume><spage>1</spage><epage>11</epage><pages>1-11</pages><issn>1024-123X</issn><eissn>1563-5147</eissn><abstract>This paper studies association rule mining and decision tree algorithm, focusing on the extended research of association rule mining, including the number of generated rules, mining association rules of long itemsets with low support, attribute selection criteria and multivalue attributes in decision tree algorithm. This paper conducts in-depth research and analysis on the design and optimization of the mental health education consultation management system using the association rule decision tree algorithm. This paper analyzes the meaning of parameters under the support-confidence-interest model, and uses regression method to design equations between the number of rules and parameters. We use the multiple correlation coefficient to test the fitting effect of the equation, and use the significance test to verify whether the coefficient of the parameter is significantly zero. On the one hand, the widely used psychological crisis prevention measures generally include the screening of the SCL psychological scale in the early stage of first-year enrolment, the holding of general psychological knowledge lectures and courses, and the opening of psychological counselling rooms with a low penetration rate, but these practices are to a certain extent. In other words, it cannot enable the student administrator to grasp the psychological status of the students in a timely, effective, and dynamic manner, to timely intervene in the possible crisis. Not only the number of attribute values of the current node is considered but also the size of the variable precision clear area of the lower node is considered, that is, the two-layer nodes of the tree are considered at the same time. The new attribute selection method not only overcomes the shortcomings of the original algorithm, but also has the advantages of variable precision rough sets. This paper uses a new criterion for attribute selection, weighted roughness and complexity, which comprehensively considers the classification accuracy and the number of branches. In order to reduce the influence of noisy data and missing values, the algorithm uses a class prediction method based on matching degree. Through comparative experiments, the effectiveness of the method proposed in this paper is verified. We propose a new calculation formula for the similarity of the child nodes of the label set to evaluate the effect of attribute classification, and comprehensively consider the situation that the elements in the two multilabel sets appear or not appear at the same time, so that the calculation of the similarity of the label set is more comprehensive. The experimental results show that the model proposed in this paper can excavate the dialectical combination of multiple factors. By comparing with the existing algorithms, the classification effect of the proposed algorithm is verified. The classification algorithm proposed in this paper is more suitable for dealing with multi-value attributes and multiclass data classification problems. The psychological evaluation and counselling system designed in this paper has achieved the expected goal. The results of this paper can improve the problems existing in the work of psychological counselling services.</abstract><cop>New York</cop><pub>Hindawi</pub><doi>10.1155/2022/7307741</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0003-3824-8944</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1024-123X
ispartof Mathematical problems in engineering, 2022, Vol.2022, p.1-11
issn 1024-123X
1563-5147
language eng
recordid cdi_proquest_journals_2648810357
source Wiley-Blackwell Titles (Open access); Publicly Available Content Database; Coronavirus Research Database
subjects Algorithms
Big Data
Classification
College students
Colleges & universities
Correlation coefficients
Data analysis
Data mining
Decision analysis
Decision making
Decision trees
Design optimization
Education
Efficiency
Engineering
Evaluation
Health education
Information technology
Mental disorders
Mental health
Mental health care
Nodes
Parameters
Regression models
Similarity
title Construction and Optimization of Mental Health Education Consultation Management System Based on Decision Tree Association Rule Mining
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-11-17T09%3A31%3A02IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Construction%20and%20Optimization%20of%20Mental%20Health%20Education%20Consultation%20Management%20System%20Based%20on%20Decision%20Tree%20Association%20Rule%20Mining&rft.jtitle=Mathematical%20problems%20in%20engineering&rft.au=Liu,%20Haoxian&rft.date=2022&rft.volume=2022&rft.spage=1&rft.epage=11&rft.pages=1-11&rft.issn=1024-123X&rft.eissn=1563-5147&rft_id=info:doi/10.1155/2022/7307741&rft_dat=%3Cproquest_cross%3E2648810357%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c2521-29e1993f621869a9bc727e2b339ae5b1886fa347785588a70b27181a17fa3dab3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2648810357&rft_id=info:pmid/&rfr_iscdi=true