Mental health analysis of international students using machine learning techniques
International students' mental health has become an increasing concern in recent years, as more students leave their country for better education. They experience a wide range of challenges while studying abroad that have an impact on their psychological well-being. These challenges can include...
Gespeichert in:
| Veröffentlicht in: | PloS one 2024-06, Vol.19 (6), p.e0304132 |
|---|---|
| Hauptverfasser: | , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | 6 |
| container_start_page | e0304132 |
| container_title | PloS one |
| container_volume | 19 |
| creator | Rahman, Muhammad Azizur Kohli, Tripti |
| description | International students' mental health has become an increasing concern in recent years, as more students leave their country for better education. They experience a wide range of challenges while studying abroad that have an impact on their psychological well-being. These challenges can include language obstacles, cultural differences, homesickness, financial issues and other elements that could severely impact the mental health of international students. Given the limited research on the demographic, cultural, and psychosocial variables that influence international students' mental health, and the scarcity of studies on the use of machine learning algorithms in this area, this study aimed to analyse data to understand the demographic, cultural factors, and psychosocial factors that impact mental health of international students. Additionally, this paper aimed to build a machine learning-based model for predicting depression among international students in the United Kingdom. This study utilized both primary data gathered through an online survey questionnaire targeted at international students and secondary data was sourced from the 'A Dataset of Students' Mental Health and Help-Seeking Behaviors in a Multicultural Environment,' focusing exclusively on international student data within this dataset. We conducted data analysis on the primary data and constructed models using the secondary data for predicting depression among international students. The secondary dataset is divided into training (70%) and testing (30%) sets for analysis, employing four machine learning models: Logistic Regression, Decision Tree, Random Forest, and K Nearest Neighbor. To assess each algorithm's performance, we considered metrics such as Accuracy, Sensitivity, Specificity, Precision and AU-ROC curve. This study identifies significant demographic variables (e.g., loan status, gender, age, marital status) and psychosocial factors (financial difficulties, academic stress, homesickness, loneliness) contributing to international students' mental health. Among the machine learning models, the Random Forest model demonstrated the highest accuracy, achieving an 80% accuracy rate in predicting depression. |
| doi_str_mv | 10.1371/journal.pone.0304132 |
| format | Article |
| fullrecord | <record><control><sourceid>gale_plos_</sourceid><recordid>TN_cdi_plos_journals_3069270015</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A796651960</galeid><sourcerecordid>A796651960</sourcerecordid><originalsourceid>FETCH-LOGICAL-c576t-9c1967d77ec16b995df54b62f27b9db911f27ee265a25ce120cfd9c414d80c6b3</originalsourceid><addsrcrecordid>eNqNkttu1DAQhiMEoqXwBggiISG42MWH2F5foariUKmoUjncWo4z2bhy7CV2EH17HDatNqgXyBe2xt_8Mx7_RfEcozWmAr-7DuPgtVvvgoc1oqjClDwojrGkZMUJog8PzkfFkxivEWJ0w_nj4ohuNhXFFTourr6AT9qVHWiXulJnxZtoYxna0voEuUKyIQfLmMYmo7Eco_Xbstemsx5KB3rwUyCB6bz9OUJ8WjxqtYvwbN5Piu8fP3w7-7y6uPx0fnZ6sTJM8LSSBksuGiHAYF5LyZqWVTUnLRG1bGqJcT4BEM40YQYwQaZtpKlw1WyQ4TU9KV7udXcuRDWPIyqKuCQCIcwy8X4mxrqHxuT-B-3UbrC9Hm5U0FYtb7zt1Db8Uhhjxmk1KbyZFYYwvS2p3kYDzmkPYfxbjEkhBEYZffUPen9LM7XVDpT1bciFzSSqToXknOWhTFrre6i8GuityR_e2hxfJLxdJGQmwe-01WOM6vzr1f-zlz-W7OsDdm-SGNw4mSIuwWoPmiHEOEB7N2WM1OTX22moya9q9mtOe3H4Q3dJtwalfwBkJ-ZO</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3069270015</pqid></control><display><type>article</type><title>Mental health analysis of international students using machine learning techniques</title><source>MEDLINE</source><source>DOAJ Directory of Open Access Journals</source><source>Public Library of Science (PLoS) Journals Open Access</source><source>EZB-FREE-00999 freely available EZB journals</source><source>PubMed Central</source><source>Free Full-Text Journals in Chemistry</source><creator>Rahman, Muhammad Azizur ; Kohli, Tripti</creator><contributor>Alemayehu, Yadeta</contributor><creatorcontrib>Rahman, Muhammad Azizur ; Kohli, Tripti ; Alemayehu, Yadeta</creatorcontrib><description>International students' mental health has become an increasing concern in recent years, as more students leave their country for better education. They experience a wide range of challenges while studying abroad that have an impact on their psychological well-being. These challenges can include language obstacles, cultural differences, homesickness, financial issues and other elements that could severely impact the mental health of international students. Given the limited research on the demographic, cultural, and psychosocial variables that influence international students' mental health, and the scarcity of studies on the use of machine learning algorithms in this area, this study aimed to analyse data to understand the demographic, cultural factors, and psychosocial factors that impact mental health of international students. Additionally, this paper aimed to build a machine learning-based model for predicting depression among international students in the United Kingdom. This study utilized both primary data gathered through an online survey questionnaire targeted at international students and secondary data was sourced from the 'A Dataset of Students' Mental Health and Help-Seeking Behaviors in a Multicultural Environment,' focusing exclusively on international student data within this dataset. We conducted data analysis on the primary data and constructed models using the secondary data for predicting depression among international students. The secondary dataset is divided into training (70%) and testing (30%) sets for analysis, employing four machine learning models: Logistic Regression, Decision Tree, Random Forest, and K Nearest Neighbor. To assess each algorithm's performance, we considered metrics such as Accuracy, Sensitivity, Specificity, Precision and AU-ROC curve. This study identifies significant demographic variables (e.g., loan status, gender, age, marital status) and psychosocial factors (financial difficulties, academic stress, homesickness, loneliness) contributing to international students' mental health. Among the machine learning models, the Random Forest model demonstrated the highest accuracy, achieving an 80% accuracy rate in predicting depression.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0304132</identifier><identifier>PMID: 38843140</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Accuracy ; Adolescent ; Adult ; Algorithms ; Analysis ; Anxiety ; College students ; Computer and Information Sciences ; Cultural factors ; Data analysis ; Data mining ; Datasets ; Decision trees ; Demographic variables ; Demographics ; Demography ; Depression - diagnosis ; Earthquakes ; Female ; Foreign students ; Humans ; Information management ; Learning algorithms ; Machine Learning ; Male ; Medicine and Health Sciences ; Mental depression ; Mental disorders ; Mental Health ; Methods ; Physical Sciences ; Post traumatic stress disorder ; Psychological aspects ; Psychological factors ; Questionnaires ; Regression analysis ; Research and Analysis Methods ; Social isolation ; Social Sciences ; Students ; Students - psychology ; Students, Foreign ; Support vector machines ; Surveys and Questionnaires ; United Kingdom ; Wavelet transforms ; Well being ; Young Adult</subject><ispartof>PloS one, 2024-06, Vol.19 (6), p.e0304132</ispartof><rights>Copyright: © 2024 Rahman, Kohli. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</rights><rights>COPYRIGHT 2024 Public Library of Science</rights><rights>2024 Rahman, Kohli. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2024 Rahman, Kohli 2024 Rahman, Kohli</rights><rights>2024 Rahman, Kohli. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c576t-9c1967d77ec16b995df54b62f27b9db911f27ee265a25ce120cfd9c414d80c6b3</cites><orcidid>0000-0002-0805-7461</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11156345/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11156345/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2914,23846,27903,27904,53770,53772,79347,79348</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38843140$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Alemayehu, Yadeta</contributor><creatorcontrib>Rahman, Muhammad Azizur</creatorcontrib><creatorcontrib>Kohli, Tripti</creatorcontrib><title>Mental health analysis of international students using machine learning techniques</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>International students' mental health has become an increasing concern in recent years, as more students leave their country for better education. They experience a wide range of challenges while studying abroad that have an impact on their psychological well-being. These challenges can include language obstacles, cultural differences, homesickness, financial issues and other elements that could severely impact the mental health of international students. Given the limited research on the demographic, cultural, and psychosocial variables that influence international students' mental health, and the scarcity of studies on the use of machine learning algorithms in this area, this study aimed to analyse data to understand the demographic, cultural factors, and psychosocial factors that impact mental health of international students. Additionally, this paper aimed to build a machine learning-based model for predicting depression among international students in the United Kingdom. This study utilized both primary data gathered through an online survey questionnaire targeted at international students and secondary data was sourced from the 'A Dataset of Students' Mental Health and Help-Seeking Behaviors in a Multicultural Environment,' focusing exclusively on international student data within this dataset. We conducted data analysis on the primary data and constructed models using the secondary data for predicting depression among international students. The secondary dataset is divided into training (70%) and testing (30%) sets for analysis, employing four machine learning models: Logistic Regression, Decision Tree, Random Forest, and K Nearest Neighbor. To assess each algorithm's performance, we considered metrics such as Accuracy, Sensitivity, Specificity, Precision and AU-ROC curve. This study identifies significant demographic variables (e.g., loan status, gender, age, marital status) and psychosocial factors (financial difficulties, academic stress, homesickness, loneliness) contributing to international students' mental health. Among the machine learning models, the Random Forest model demonstrated the highest accuracy, achieving an 80% accuracy rate in predicting depression.</description><subject>Accuracy</subject><subject>Adolescent</subject><subject>Adult</subject><subject>Algorithms</subject><subject>Analysis</subject><subject>Anxiety</subject><subject>College students</subject><subject>Computer and Information Sciences</subject><subject>Cultural factors</subject><subject>Data analysis</subject><subject>Data mining</subject><subject>Datasets</subject><subject>Decision trees</subject><subject>Demographic variables</subject><subject>Demographics</subject><subject>Demography</subject><subject>Depression - diagnosis</subject><subject>Earthquakes</subject><subject>Female</subject><subject>Foreign students</subject><subject>Humans</subject><subject>Information management</subject><subject>Learning algorithms</subject><subject>Machine Learning</subject><subject>Male</subject><subject>Medicine and Health Sciences</subject><subject>Mental depression</subject><subject>Mental disorders</subject><subject>Mental Health</subject><subject>Methods</subject><subject>Physical Sciences</subject><subject>Post traumatic stress disorder</subject><subject>Psychological aspects</subject><subject>Psychological factors</subject><subject>Questionnaires</subject><subject>Regression analysis</subject><subject>Research and Analysis Methods</subject><subject>Social isolation</subject><subject>Social Sciences</subject><subject>Students</subject><subject>Students - psychology</subject><subject>Students, Foreign</subject><subject>Support vector machines</subject><subject>Surveys and Questionnaires</subject><subject>United Kingdom</subject><subject>Wavelet transforms</subject><subject>Well being</subject><subject>Young Adult</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqNkttu1DAQhiMEoqXwBggiISG42MWH2F5foariUKmoUjncWo4z2bhy7CV2EH17HDatNqgXyBe2xt_8Mx7_RfEcozWmAr-7DuPgtVvvgoc1oqjClDwojrGkZMUJog8PzkfFkxivEWJ0w_nj4ohuNhXFFTourr6AT9qVHWiXulJnxZtoYxna0voEuUKyIQfLmMYmo7Eco_Xbstemsx5KB3rwUyCB6bz9OUJ8WjxqtYvwbN5Piu8fP3w7-7y6uPx0fnZ6sTJM8LSSBksuGiHAYF5LyZqWVTUnLRG1bGqJcT4BEM40YQYwQaZtpKlw1WyQ4TU9KV7udXcuRDWPIyqKuCQCIcwy8X4mxrqHxuT-B-3UbrC9Hm5U0FYtb7zt1Db8Uhhjxmk1KbyZFYYwvS2p3kYDzmkPYfxbjEkhBEYZffUPen9LM7XVDpT1bciFzSSqToXknOWhTFrre6i8GuityR_e2hxfJLxdJGQmwe-01WOM6vzr1f-zlz-W7OsDdm-SGNw4mSIuwWoPmiHEOEB7N2WM1OTX22moya9q9mtOe3H4Q3dJtwalfwBkJ-ZO</recordid><startdate>20240606</startdate><enddate>20240606</enddate><creator>Rahman, Muhammad Azizur</creator><creator>Kohli, Tripti</creator><general>Public Library of Science</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-0805-7461</orcidid></search><sort><creationdate>20240606</creationdate><title>Mental health analysis of international students using machine learning techniques</title><author>Rahman, Muhammad Azizur ; Kohli, Tripti</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c576t-9c1967d77ec16b995df54b62f27b9db911f27ee265a25ce120cfd9c414d80c6b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Accuracy</topic><topic>Adolescent</topic><topic>Adult</topic><topic>Algorithms</topic><topic>Analysis</topic><topic>Anxiety</topic><topic>College students</topic><topic>Computer and Information Sciences</topic><topic>Cultural factors</topic><topic>Data analysis</topic><topic>Data mining</topic><topic>Datasets</topic><topic>Decision trees</topic><topic>Demographic variables</topic><topic>Demographics</topic><topic>Demography</topic><topic>Depression - diagnosis</topic><topic>Earthquakes</topic><topic>Female</topic><topic>Foreign students</topic><topic>Humans</topic><topic>Information management</topic><topic>Learning algorithms</topic><topic>Machine Learning</topic><topic>Male</topic><topic>Medicine and Health Sciences</topic><topic>Mental depression</topic><topic>Mental disorders</topic><topic>Mental Health</topic><topic>Methods</topic><topic>Physical Sciences</topic><topic>Post traumatic stress disorder</topic><topic>Psychological aspects</topic><topic>Psychological factors</topic><topic>Questionnaires</topic><topic>Regression analysis</topic><topic>Research and Analysis Methods</topic><topic>Social isolation</topic><topic>Social Sciences</topic><topic>Students</topic><topic>Students - psychology</topic><topic>Students, Foreign</topic><topic>Support vector machines</topic><topic>Surveys and Questionnaires</topic><topic>United Kingdom</topic><topic>Wavelet transforms</topic><topic>Well being</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Rahman, Muhammad Azizur</creatorcontrib><creatorcontrib>Kohli, Tripti</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Agricultural Science Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>Materials Science 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><collection>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Rahman, Muhammad Azizur</au><au>Kohli, Tripti</au><au>Alemayehu, Yadeta</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Mental health analysis of international students using machine learning techniques</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2024-06-06</date><risdate>2024</risdate><volume>19</volume><issue>6</issue><spage>e0304132</spage><pages>e0304132-</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>International students' mental health has become an increasing concern in recent years, as more students leave their country for better education. They experience a wide range of challenges while studying abroad that have an impact on their psychological well-being. These challenges can include language obstacles, cultural differences, homesickness, financial issues and other elements that could severely impact the mental health of international students. Given the limited research on the demographic, cultural, and psychosocial variables that influence international students' mental health, and the scarcity of studies on the use of machine learning algorithms in this area, this study aimed to analyse data to understand the demographic, cultural factors, and psychosocial factors that impact mental health of international students. Additionally, this paper aimed to build a machine learning-based model for predicting depression among international students in the United Kingdom. This study utilized both primary data gathered through an online survey questionnaire targeted at international students and secondary data was sourced from the 'A Dataset of Students' Mental Health and Help-Seeking Behaviors in a Multicultural Environment,' focusing exclusively on international student data within this dataset. We conducted data analysis on the primary data and constructed models using the secondary data for predicting depression among international students. The secondary dataset is divided into training (70%) and testing (30%) sets for analysis, employing four machine learning models: Logistic Regression, Decision Tree, Random Forest, and K Nearest Neighbor. To assess each algorithm's performance, we considered metrics such as Accuracy, Sensitivity, Specificity, Precision and AU-ROC curve. This study identifies significant demographic variables (e.g., loan status, gender, age, marital status) and psychosocial factors (financial difficulties, academic stress, homesickness, loneliness) contributing to international students' mental health. Among the machine learning models, the Random Forest model demonstrated the highest accuracy, achieving an 80% accuracy rate in predicting depression.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>38843140</pmid><doi>10.1371/journal.pone.0304132</doi><tpages>e0304132</tpages><orcidid>https://orcid.org/0000-0002-0805-7461</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2024-06, Vol.19 (6), p.e0304132 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_3069270015 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Public Library of Science (PLoS) Journals Open Access; EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Accuracy Adolescent Adult Algorithms Analysis Anxiety College students Computer and Information Sciences Cultural factors Data analysis Data mining Datasets Decision trees Demographic variables Demographics Demography Depression - diagnosis Earthquakes Female Foreign students Humans Information management Learning algorithms Machine Learning Male Medicine and Health Sciences Mental depression Mental disorders Mental Health Methods Physical Sciences Post traumatic stress disorder Psychological aspects Psychological factors Questionnaires Regression analysis Research and Analysis Methods Social isolation Social Sciences Students Students - psychology Students, Foreign Support vector machines Surveys and Questionnaires United Kingdom Wavelet transforms Well being Young Adult |
| title | Mental health analysis of international students using machine learning techniques |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-22T08%3A23%3A25IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Mental%20health%20analysis%20of%20international%20students%20using%20machine%20learning%20techniques&rft.jtitle=PloS%20one&rft.au=Rahman,%20Muhammad%20Azizur&rft.date=2024-06-06&rft.volume=19&rft.issue=6&rft.spage=e0304132&rft.pages=e0304132-&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0304132&rft_dat=%3Cgale_plos_%3EA796651960%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3069270015&rft_id=info:pmid/38843140&rft_galeid=A796651960&rfr_iscdi=true |