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Machine learning approaches for predicting suicidal behaviors among university students in Bangladesh during the COVID-19 pandemic: A cross-sectional study
Psychological and behavioral stress has increased enormously during Coronavirus Disease 2019 (COVID-19) pandemic. However, early prediction and intervention to address psychological distress and suicidal behaviors are crucial to prevent suicide-related deaths. This study aimed to develop a machine a...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Lippincott Williams & Wilkins
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10343891/ https://www.ncbi.nlm.nih.gov/pubmed/37443501 http://dx.doi.org/10.1097/MD.0000000000034285 |
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author | Mahmud, Sultan Mohsin, Md Muyeed, Abdul Nazneen, Shaila Abu Sayed, Md. Murshed, Nabil Tonmon, Tajrin Tahrin Islam, Ariful |
author_facet | Mahmud, Sultan Mohsin, Md Muyeed, Abdul Nazneen, Shaila Abu Sayed, Md. Murshed, Nabil Tonmon, Tajrin Tahrin Islam, Ariful |
author_sort | Mahmud, Sultan |
collection | PubMed |
description | Psychological and behavioral stress has increased enormously during Coronavirus Disease 2019 (COVID-19) pandemic. However, early prediction and intervention to address psychological distress and suicidal behaviors are crucial to prevent suicide-related deaths. This study aimed to develop a machine algorithm to predict suicidal behaviors and identify essential predictors of suicidal behaviors among university students in Bangladesh during the COVID-19 pandemic. An anonymous online survey was conducted among university students in Bangladesh from June 1 to June 30, 2022. A total of 2391 university students completed and submitted the questionnaires. Five different Machine Learning models (MLMs) were applied to develop a suitable algorithm for predicting suicidal behaviors among university students. In predicting suicidal behaviors, the most crucial background and demographic features were relationship status, friendly environment in the family, family income, family type, and sex. In addition, features related to the impact of the COVID-19 pandemic were identified as job loss, economic loss, and loss of family/relatives due to COVID-19. Moreover, factors related to mental health include depression, anxiety, stress, and insomnia. The performance evaluation and comparison of the MLM showed that all models behaved consistently and were comparable in predicting suicidal risk. However, the Support Vector Machine was the best and most consistent performing model among all MLMs in terms of accuracy (79%), Kappa (0.59), receiver operating characteristic (0.89), sensitivity (0.81), and specificity (0.81). Support Vector Machine is the best-performing model for predicting suicidal risks among university students in Bangladesh and can help in designing appropriate and timely suicide prevention interventions. |
format | Online Article Text |
id | pubmed-10343891 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-103438912023-07-14 Machine learning approaches for predicting suicidal behaviors among university students in Bangladesh during the COVID-19 pandemic: A cross-sectional study Mahmud, Sultan Mohsin, Md Muyeed, Abdul Nazneen, Shaila Abu Sayed, Md. Murshed, Nabil Tonmon, Tajrin Tahrin Islam, Ariful Medicine (Baltimore) 5000 Psychological and behavioral stress has increased enormously during Coronavirus Disease 2019 (COVID-19) pandemic. However, early prediction and intervention to address psychological distress and suicidal behaviors are crucial to prevent suicide-related deaths. This study aimed to develop a machine algorithm to predict suicidal behaviors and identify essential predictors of suicidal behaviors among university students in Bangladesh during the COVID-19 pandemic. An anonymous online survey was conducted among university students in Bangladesh from June 1 to June 30, 2022. A total of 2391 university students completed and submitted the questionnaires. Five different Machine Learning models (MLMs) were applied to develop a suitable algorithm for predicting suicidal behaviors among university students. In predicting suicidal behaviors, the most crucial background and demographic features were relationship status, friendly environment in the family, family income, family type, and sex. In addition, features related to the impact of the COVID-19 pandemic were identified as job loss, economic loss, and loss of family/relatives due to COVID-19. Moreover, factors related to mental health include depression, anxiety, stress, and insomnia. The performance evaluation and comparison of the MLM showed that all models behaved consistently and were comparable in predicting suicidal risk. However, the Support Vector Machine was the best and most consistent performing model among all MLMs in terms of accuracy (79%), Kappa (0.59), receiver operating characteristic (0.89), sensitivity (0.81), and specificity (0.81). Support Vector Machine is the best-performing model for predicting suicidal risks among university students in Bangladesh and can help in designing appropriate and timely suicide prevention interventions. Lippincott Williams & Wilkins 2023-07-14 /pmc/articles/PMC10343891/ /pubmed/37443501 http://dx.doi.org/10.1097/MD.0000000000034285 Text en Copyright © 2023 the Author(s). Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by-nc/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC) (https://creativecommons.org/licenses/by-nc/4.0/) , where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal. |
spellingShingle | 5000 Mahmud, Sultan Mohsin, Md Muyeed, Abdul Nazneen, Shaila Abu Sayed, Md. Murshed, Nabil Tonmon, Tajrin Tahrin Islam, Ariful Machine learning approaches for predicting suicidal behaviors among university students in Bangladesh during the COVID-19 pandemic: A cross-sectional study |
title | Machine learning approaches for predicting suicidal behaviors among university students in Bangladesh during the COVID-19 pandemic: A cross-sectional study |
title_full | Machine learning approaches for predicting suicidal behaviors among university students in Bangladesh during the COVID-19 pandemic: A cross-sectional study |
title_fullStr | Machine learning approaches for predicting suicidal behaviors among university students in Bangladesh during the COVID-19 pandemic: A cross-sectional study |
title_full_unstemmed | Machine learning approaches for predicting suicidal behaviors among university students in Bangladesh during the COVID-19 pandemic: A cross-sectional study |
title_short | Machine learning approaches for predicting suicidal behaviors among university students in Bangladesh during the COVID-19 pandemic: A cross-sectional study |
title_sort | machine learning approaches for predicting suicidal behaviors among university students in bangladesh during the covid-19 pandemic: a cross-sectional study |
topic | 5000 |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10343891/ https://www.ncbi.nlm.nih.gov/pubmed/37443501 http://dx.doi.org/10.1097/MD.0000000000034285 |
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