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Important Features Associated with Depression Prediction and Explainable AI

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Well-Being in the Information Society: When the Mind Breaks (WIS 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1626))

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Abstract

Depression is a debilitating disease that leaves individuals persistently feeling sad or hopeless for more than two weeks affecting more than 300 million people globally. We applied several machine learning models with model explainability to a publicly available depression dataset. Several experiments were performed to assess the use of feature selection methods and technique to address dataset imbalance on diagnostic accuracy. The top performing model was obtained by logistic regression with excellent performance metrics (91% accuracy, 93% sensitivity, 85% specificity, 93% precision, 93% F1-score and 0.78 Matthews correlation coefficient). Feature importance was also generated for the best model. Explainable artificial intelligence method using LIME was applied to help understand the reasoning behind the model’s classification of depression leading to better understanding of physicians, thus demonstrating its use in clinical practice.

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Authors and Affiliations

  1. Department of Physical Sciences and Mathematics, University of the Philippines Manila, Manila, Philippines

    Vincent Peter C. Magboo & Ma. Sheila A. Magboo

Authors
  1. Vincent Peter C. Magboo
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  2. Ma. Sheila A. Magboo
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Correspondence to Vincent Peter C. Magboo .

Editor information

Editors and Affiliations

  1. Tampere University, Tampere, Finland

    Hongxiu Li

  2. Ã…bo Akademi University, Turku, Finland

    Maehed Ghorbanian Zolbin

  3. University of Tartu, Tartu, Estonia

    Robert Krimmer

  4. Finnish Institute for Health and Welfare, Helsinki, Finland

    Jukka Kärkkäinen

  5. Tampere University, Tampere, Finland

    Chenglong Li

  6. University of Turku, Turku, Finland

    Reima Suomi

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Magboo, V.P.C., Magboo, M.S.A. (2022). Important Features Associated with Depression Prediction and Explainable AI. In: Li, H., Ghorbanian Zolbin, M., Krimmer, R., Kärkkäinen, J., Li, C., Suomi, R. (eds) Well-Being in the Information Society: When the Mind Breaks. WIS 2022. Communications in Computer and Information Science, vol 1626. Springer, Cham. https://doi.org/10.1007/978-3-031-14832-3_2

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  • DOI: https://doi.org/10.1007/978-3-031-14832-3_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-14831-6

  • Online ISBN: 978-3-031-14832-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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