Evidence map›Paper›PMID 40750870›Full record

ArticleBMC psychiatry2025

Exploring suicidal thoughts among prospective university students: a study with applications of machine learning and GIS techniques.

Mohammed A Mamun, Firoj Al-Mamun, Md Emran Hasan, Nitai Roy, Moneerah Mohammad ALmerab, David Gozal, Md Shakhaoat Hossain

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Article in BMC psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Mohammed A MamunDepartment of Public Health and Informatics, Jahangirnagar University, Dhaka, Bangladesh. mamun@thechinta.org.
Firoj Al-MamunDepartment of Public Health and Informatics, Jahangirnagar University, Dhaka, Bangladesh.
Md Emran HasanCHINTA Research Bangladesh, Savar, Dhaka, 1342, Bangladesh.
Nitai RoyDepartment of Biochemistry and Molecular Biology, Patuakhali Science and Technology University, Patuakhali, Bangladesh.
Moneerah Mohammad ALmerabDepartment of Psychology, College of Education and Human Development, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia.
David GozalOffice of The Dean and Department of Pediatrics, Joan C. Edwards School of Medicine, Marshall University, 1600 Medical Center Dr, Huntington, WV, 25701, USA.
Md Shakhaoat HossainDepartment of Public Health and Informatics, Jahangirnagar University, Dhaka, Bangladesh.

Funding

NIH National Institutes of Health HL-166617NIH National Institutes of Health HL-169266NIH National Institutes of Health P20-GM103434
6 · The paper itself

Abstract

backgroundProspective 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.

methodsData 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.

resultsThe 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.

conclusionsThis 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.

Indexed as

Geographic Information SystemsMachine LearningStudentsSuicidal IdeationAdolescentAdultFemaleHumansMalePrevalenceProspective StudiesRisk AssessmentRisk FactorsUniversitiesYoung AdultCatBoostMachine learningPredictive modelingSpatial analysisSuicidal behaviorUniversity students

Identifiers

PMID40750870
PMCPMC12317646

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