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Machine Learning Classification Algorithms for Predicting Depression Among University Students in Bangladesh

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Proceedings of the Third International Conference on Trends in Computational and Cognitive Engineering

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 348))

Abstract

Depression is a dreadful mental disorder affecting negatively one’s way of thinking and ability of functioning while the person may not be aware of it. The prevalence of depression is high among the young generation of developing countries because of ever-increasing academic and career-related distress, job uncertainty, and family problems. In Bangladesh, there is a dearth of information about the predictors of depression among university students, so is a model to identify them. The information is important for the prevention of depression and the promotion of mental health. The data set we used for this research was built on the data collected by a questionnaire circulated through social media. Using Pearson’s chi-squared test and back elimination method, we have identified the key feature variables. We used six different ML classifiers to build the classification model that is capable of detecting the presence of depression. Among the six, the stacking classifier with 24 attributes shows the highest accuracy.

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Author information

Authors and Affiliations

  1. Department of Information and Communication Technology, Bangladesh University of Professionals, Dhaka, 1216, Bangladesh

    Uwasila Binte Munir

  2. Institute of Information Technology, Jahangirnagar University, Savar, Dhaka, 1342, Bangladesh

    M. Shamim Kaiser

  3. Chittagong University of Engineering and Technology, Chittagong, 4349, Bangladesh

    Uwaise Ibna Islam

  4. Dhaka University of Engineering and Technology, Gazipur, 1707, Bangladesh

    Fazlul Hasan Siddiqui

Authors
  1. Uwasila Binte Munir
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  2. M. Shamim Kaiser
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  3. Uwaise Ibna Islam
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  4. Fazlul Hasan Siddiqui
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Corresponding author

Correspondence to M. Shamim Kaiser .

Editor information

Editors and Affiliations

  1. Jahangirnagar University, Dhaka, Bangladesh

    M. Shamim Kaiser

  2. Amity University Rajasthan, Jaipur, India

    Kanad Ray

  3. Surface Characterization Group, National Institute for Materials Science, Tsukuba, Japan

    Anirban Bandyopadhyay

  4. Faculty of Applied Sciences and Technology, Tun Hussein Onn University of Malaysia, Parit Raja, Johor, Malaysia

    Kavikumar Jacob

  5. Faculty of Applied Sciences and Technology (FAST), Universiti Tun Hussein Onn Malaysia, Johor, Malaysia

    Kek Sie Long

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© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Munir, U.B., Kaiser, M.S., Islam, U.I., Siddiqui, F.H. (2022). Machine Learning Classification Algorithms for Predicting Depression Among University Students in Bangladesh. In: Kaiser, M.S., Ray, K., Bandyopadhyay, A., Jacob, K., Long, K.S. (eds) Proceedings of the Third International Conference on Trends in Computational and Cognitive Engineering. Lecture Notes in Networks and Systems, vol 348. Springer, Singapore. https://doi.org/10.1007/978-981-16-7597-3_6

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