Predictive Modeling of Comorbid Depression and Anxiety Symptoms Among Prospective University Students: A GIS-Based and Machine Learning Study.

Journal: Psychological reports
Published Date: Jul 13, 2025

Abstract

Prospective university students are highly susceptible to mental health issues such as depression and anxiety. This study investigates the prevalence and risk factors associated with comorbid depression and anxiety suffering, integrating GIS and Machine Learning techniques to comprehensively understand their spatial distribution and predictive factors. Data from 1485 participants were collected via a cross-sectional survey, encompassing socio-demographic details, educational backgrounds, psychological factors, and geographic locations. Statistical analyses utilized SPSS for traditional methods, Python for supervised classification algorithms in machine learning, and R (version 4.3.1) with the 'bangladesh' package for GIS analyses. The study revealed that 29.0% of the participants experienced comorbid depression and anxiety. Key risk factors identified included female gender, urban residency, joint family structures, commerce educational backgrounds, self-coaching for admission, dissatisfaction with mock tests, students higher monthly expenditure, past-year suicidal ideation, and a history of mental health issues. In machine learning, XGBoost SHAP identified past-year suicidal ideation as the most significant predictor, while history of suicide completion in family had minimal impact. The GBM model demonstrated high accuracy, precision, and F1-score metrics, whereas CatBoost excelled in logarithmic loss. GIS mapping highlighted regional disparities, particularly in districts within the Chittagong Hill Tracts and other districts showing elevated prevalence rates. This study highlights the need for targeted interventions and supportive policies to address the prevalent mental health challenges among university entrants, which requires collaborative efforts among educational institutions, healthcare providers, policymakers, and local communities.

Authors

  • Mohammed A Mamun
    CHINTA Research Bangladesh, Savar, Dhaka, Bangladesh.
  • Firoj Al-Mamun
    CHINTA Research Bangladesh, Savar, Dhaka, Bangladesh.
  • Md Emran Hasan
    CHINTA Research Bangladesh, Savar, Dhaka, Bangladesh.
  • Md Hasibul Islam Jitu
    Institute of Statistical Research and Training, University of Dhaka, Dhaka, Bangladesh.
  • Muzibul Haque Limon
    Department of Public Health and Informatics, Jahangirnagar University, Dhaka, Bangladesh.
  • Nahida Bintee Mostofa
    CHINTA Research Bangladesh, Dhaka, Bangladesh.
  • Tamim Ikram
    CHINTA Research Bangladesh, Dhaka, Bangladesh.
  • Marjia Khan Trisha
    CHINTA Research Bangladesh, Dhaka, Bangladesh.
  • Tasnim B K Chowdhury
    CHINTA Research Bangladesh, Dhaka, Bangladesh.
  • Nobendo Paul Shanto
    Department of Public Health and Informatics, Jahangirnagar University, Dhaka, Bangladesh.
  • Moneerah Mohammad ALmerab
    Department of Psychology, College of Education and Human Development, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia.
  • Nitai Roy
    Department of Biochemistry and Food Analysis, Patuakhali Science and Technology University, Patuakhali, Bangladesh.
  • David Gozal
    Joan C. Edwards School of Medicine, Marshall University, Huntington, WV, United States.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals