Exploring suicidal thoughts among prospective university students: a study with applications of machine learning and GIS techniques
Abstract Background Prospective university students are regarded as highly vulnerable to psychological issues, including suicide. Despite the complexity of suicidal behaviors, innovative methodologies like Geographic Information System (GIS) mapping and Machine Learning have not been fully explored...
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| Format: | Article |
| Language: | English |
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BMC
2025-08-01
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| Series: | BMC Psychiatry |
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| Online Access: | https://doi.org/10.1186/s12888-025-07188-2 |
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| author | Mohammed A. Mamun Firoj Al-Mamun Md Emran Hasan Nitai Roy Moneerah Mohammad ALmerab David Gozal Md. Shakhaoat Hossain |
| author_facet | Mohammed A. Mamun Firoj Al-Mamun Md Emran Hasan Nitai Roy Moneerah Mohammad ALmerab David Gozal Md. Shakhaoat Hossain |
| author_sort | Mohammed A. Mamun |
| collection | DOAJ |
| description | Abstract Background Prospective university students are regarded as highly vulnerable to psychological issues, including suicide. Despite the complexity of suicidal behaviors, innovative methodologies like Geographic Information System (GIS) mapping and Machine Learning have not been fully explored for predictive modeling and risk assessment. This study aims to investigate the prevalence and risk factors associated with suicidal behavior, offering a thorough understanding of the spatial distribution and predictive factors of suicidality. Methods Data from 1,485 participants were collected on socio-demographic characteristics, admission-related variables, health behaviors, and familial factors. Logistic regression analysis identified significant risk factors, while Machine Learning algorithms, including CatBoost and K-Nearest Neighbors, were used for prediction. Results The findings revealed a 20.5% prevalence of suicidal thoughts, with disparities across demographics and behaviors. Female participants, rural dwellers, and those from joint families exhibited higher suicidality rates. Repeat test-takers, academically struggling students, and those not coached professionally displayed elevated risks. Moreover, substance use, mental health issues, and family mental health and suicide history increased odds of suicidal behavior. GIS mapping identified regional variations, notably in the Sylhet division and Chittagong Hill Tracts. While, Machine Learning models were used to predict suicidal thoughts, with depression status as the most influential factor. Among all models, CatBoost achieved the best overall performance, with the lowest log loss, highest AUC, and strongest 95% confidence interval. KNN also performed well in accuracy, precision, and F1-score, but showed a slightly higher log loss, making CatBoost the most reliable model for predicting suicidal thoughts. Conclusions This study emphasizes the multifaceted nature of suicidal behavior, emphasizing the need for targeted interventions and support services to address mental health challenges and prevent suicide in this vulnerable population. |
| format | Article |
| id | doaj-art-d0a5be271249437db5cdf6bca3181c0c |
| institution | DOAJ |
| issn | 1471-244X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Psychiatry |
| spelling | doaj-art-d0a5be271249437db5cdf6bca3181c0c2025-08-20T03:05:16ZengBMCBMC Psychiatry1471-244X2025-08-0125112110.1186/s12888-025-07188-2Exploring suicidal thoughts among prospective university students: a study with applications of machine learning and GIS techniquesMohammed A. Mamun0Firoj Al-Mamun1Md Emran Hasan2Nitai Roy3Moneerah Mohammad ALmerab4David Gozal5Md. Shakhaoat Hossain6Department of Public Health and Informatics, Jahangirnagar UniversityDepartment of Public Health and Informatics, Jahangirnagar UniversityCHINTA Research BangladeshDepartment of Biochemistry and Molecular Biology, Patuakhali Science and Technology UniversityDepartment of Psychology, College of Education and Human Development, Princess Nourah Bint Abdulrahman UniversityOffice of The Dean and Department of Pediatrics, Joan C. Edwards School of Medicine, Marshall UniversityDepartment of Public Health and Informatics, Jahangirnagar UniversityAbstract Background Prospective university students are regarded as highly vulnerable to psychological issues, including suicide. Despite the complexity of suicidal behaviors, innovative methodologies like Geographic Information System (GIS) mapping and Machine Learning have not been fully explored for predictive modeling and risk assessment. This study aims to investigate the prevalence and risk factors associated with suicidal behavior, offering a thorough understanding of the spatial distribution and predictive factors of suicidality. Methods Data from 1,485 participants were collected on socio-demographic characteristics, admission-related variables, health behaviors, and familial factors. Logistic regression analysis identified significant risk factors, while Machine Learning algorithms, including CatBoost and K-Nearest Neighbors, were used for prediction. Results The findings revealed a 20.5% prevalence of suicidal thoughts, with disparities across demographics and behaviors. Female participants, rural dwellers, and those from joint families exhibited higher suicidality rates. Repeat test-takers, academically struggling students, and those not coached professionally displayed elevated risks. Moreover, substance use, mental health issues, and family mental health and suicide history increased odds of suicidal behavior. GIS mapping identified regional variations, notably in the Sylhet division and Chittagong Hill Tracts. While, Machine Learning models were used to predict suicidal thoughts, with depression status as the most influential factor. Among all models, CatBoost achieved the best overall performance, with the lowest log loss, highest AUC, and strongest 95% confidence interval. KNN also performed well in accuracy, precision, and F1-score, but showed a slightly higher log loss, making CatBoost the most reliable model for predicting suicidal thoughts. Conclusions This study emphasizes the multifaceted nature of suicidal behavior, emphasizing the need for targeted interventions and support services to address mental health challenges and prevent suicide in this vulnerable population.https://doi.org/10.1186/s12888-025-07188-2Suicidal behaviorUniversity studentsSpatial analysisMachine learningCatBoostPredictive modeling |
| spellingShingle | Mohammed A. Mamun Firoj Al-Mamun Md Emran Hasan Nitai Roy Moneerah Mohammad ALmerab David Gozal Md. Shakhaoat Hossain Exploring suicidal thoughts among prospective university students: a study with applications of machine learning and GIS techniques BMC Psychiatry Suicidal behavior University students Spatial analysis Machine learning CatBoost Predictive modeling |
| title | Exploring suicidal thoughts among prospective university students: a study with applications of machine learning and GIS techniques |
| title_full | Exploring suicidal thoughts among prospective university students: a study with applications of machine learning and GIS techniques |
| title_fullStr | Exploring suicidal thoughts among prospective university students: a study with applications of machine learning and GIS techniques |
| title_full_unstemmed | Exploring suicidal thoughts among prospective university students: a study with applications of machine learning and GIS techniques |
| title_short | Exploring suicidal thoughts among prospective university students: a study with applications of machine learning and GIS techniques |
| title_sort | exploring suicidal thoughts among prospective university students a study with applications of machine learning and gis techniques |
| topic | Suicidal behavior University students Spatial analysis Machine learning CatBoost Predictive modeling |
| url | https://doi.org/10.1186/s12888-025-07188-2 |
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