Skip to main content
SpringerLink
Log in

Predicting Psychological Distress from Ecological Factors: A Machine Learning Approach

  • Conference paper
  • First Online:
Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices (IEA/AIE 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12798))

Abstract

Over 300 million people worldwide were suffering from depression in 2017. Australia alone invests more than $9.1 billion each year on mental health related services. Traditional intervention methods require patients to first present with symptoms before diagnosis, leading to a reactive approach. A more proactive approach to this problem is highly desirable, and despite ongoing work using approaches such as machine learning, further work is required. This paper aims to provide a foundation by building a machine learning model across multiple techniques to predict psychological distress from ecological factors alone. Eight different classification techniques were implemented on a sample dataset, with the best results achieved through Logistic Regression, providing an accuracy of 0.811. The preliminary results suggest that, with future improvements to implementation and analysis, an accurate and reliable model is possible. This study, with the proposed base model, can potentially lead to the development of a proactive solution to the global mental health crisis.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Australian Institute of Health and Welfare: Mental health services in Australia: in brief 2019. AIHW (2019). https://doi.org/10.25816/5ec5bac5ed175

  2. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  Google Scholar 

  3. Brooks, B.L., et al.: Predicting psychological distress after pediatric concussion. J. Neurotrauma 36(5), 679–685 (2019)

    Article  Google Scholar 

  4. Budhi, G.S., Chiong, R., Pranata, I., Hu, Z.: Using machine learning to predict the sentiment of online reviews: a new framework for comparative analysis. Arch. Comput. Methods Eng. 28, 2543–2566 (2021). https://doi.org/10.1007/s11831-020-09464-8

  5. Campbell, C., Ying, Y.: Learning with Support Vector Machines. Morgan & Claypool (2011)

    Google Scholar 

  6. Chiong, R., Fan, Z., Hu, Z., Chiong, F.: Using an improved relative error support vector machine for body fat prediction. Comput. Methods Programs Biomed. 198, 105749 (2021)

    Google Scholar 

  7. Chiong, R., Satia Budhi, G., Dhakal, S.: Combining sentiment lexicons and content-based features for depression detection. IEEE Intell. Syst. 36(6) (2021)

    Google Scholar 

  8. Chollet, F., et al.: Keras (2015). https://keras.io

  9. Cornelius, B.L.R., Groothoff, J.W., van der Klink, J.J.L., Brouwer, S.: The performance of the K10, K6 and GHQ-12 to screen for present state DSM-IV disorders among disability claimants. BMC Public Health 13(1), 1–8 (2013)

    Article  Google Scholar 

  10. Dobson, A., Barnett, A.: An Introduction to Generalized Linear Models, 3rd edn. CRC Press, Boca Raton (2008)

    Book  Google Scholar 

  11. Dunteman, G., Ho, M.: Generalized linear models. In: An Introduction to Generalized Linear Models, pp. 2–6. SAGE Publications, Inc. (2011)

    Google Scholar 

  12. Every-Palmer, S., et al.: Psychological distress, anxiety, family violence, suicidality, and wellbeing in New Zealand during the COVID-19 lockdown: a cross-sectional study. PLOS ONE 15(11), e0241658 (2020)

    Google Scholar 

  13. Fatima, I., Mukhtar, H., Ahmad, H.F., Rajpoot, K.: Analysis of user-generated content from online social communities to characterise and predict depression degree. J. Inf. Sci. 44(5), 683–695 (2018)

    Article  Google Scholar 

  14. Friedman, J.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189–1232 (2001)

    Article  MathSciNet  Google Scholar 

  15. Furukawa, T.A., Kessler, R.C., Slade, T., Andrews, G.: The performance of the K6 and K10 screening scales for psychological distress in the Australian national survey of mental health and well-being. Psychol. Med. 33(2), 357–362 (2003)

    Article  Google Scholar 

  16. Galatzer-Levy, I.R., Karstoft, K.I., Statnikov, A., Shalev, A.Y.: Quantitative forecasting of PTSD from early trauma responses: a machine learning application. J. Psychiatr. Res. 59, 68–76 (2014)

    Article  Google Scholar 

  17. Hastie, T., Tibshirani, R.: Generalized Additive Models. Chapman and Hall/CRC, UK (1990)

    MATH  Google Scholar 

  18. Hunt, E., Marin, J., Stone, P.: Experiments in Induction. Academic Press, New York (1966)

    Google Scholar 

  19. Jiang, L., Li, C., Wang, S., Zhang, L.: Deep feature weighting for Naïve Bayes and its application to text classification. Eng. Appl. Artif. Intell. 52, 26–39 (2016)

    Article  Google Scholar 

  20. Kessler, R.C., et al.: Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports. Mol. Psychiatry 21(10), 1366–1371 (2016)

    Article  Google Scholar 

  21. Leightley, D., Williamson, V., Darby, J., Fear, N.T.: Identifying probable post-traumatic stress disorder: applying supervised machine learning to data from a UK military cohort. J. Ment. Health 28(1), 34–41 (2019)

    Article  Google Scholar 

  22. Loula, R., Monteiro, L.H.A.: A game theory-based model for predicting depression due to frustration in competitive environments. Comput. Math. Methods Med. 2020, 3573267 (2020)

    Article  Google Scholar 

  23. Patel, M.J., Khalaf, A., Aizenstein, H.J.: Studying depression using imaging and machine learning methods. NeuroImage Clin. 10(C), 115–123 (2016)

    Article  Google Scholar 

  24. Menard, S.: Logistic Regression: From Introductory to Advanced Concepts and Applications. SAGE, Los Angeles (2010)

    Book  Google Scholar 

  25. Mor, N.S., Dardeck, K.L.: Quantitative forecasting of risk for PTSD using ecological factors: a deep learning application. J. Soc. Behav. Health Sci. 12(1), 61–73 (2018)

    Google Scholar 

  26. Nelder, J.A., Wedderburn, R.W.: Generalized linear models. J. R. Stat. Soc. Ser. A (General) 135(3), 370–384 (1972)

    Article  Google Scholar 

  27. Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  28. Portugal, L.C., et al.: Predicting anxiety from whole brain activity patterns to emotional faces in young adults: a machine learning approach. NeuroImage Clin. 23, 101813 (2019)

    Google Scholar 

  29. Priya, A., Garg, S., Tigga, N.P.: Predicting anxiety, depression and stress in modern life using machine learning algorithms. Procedia Comput. Sci. 167, 1258–1267 (2020)

    Article  Google Scholar 

  30. Quinlan, J.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)

    Google Scholar 

  31. Rokach, L., Maimon, O.: Data Mining with Decision Trees: Theory and Applications. World Scientific Publishing, Singapore (2007)

    Book  Google Scholar 

  32. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation. Technical report, California Univ San Diego La Jolla Inst for Cognitive Science (1985)

    Google Scholar 

  33. Sau, A., Bhakta, I.: Screening of anxiety and depression among the seafarers using machine learning technology. Inform. Med. Unlocked 16, 100149 (2019)

    Google Scholar 

  34. Trotzek, M., Koitka, S., Friedrich, C.M.: Utilizing neural networks and linguistic metadata for early detection of depression indications in text sequences. IEEE Trans. Knowl. Data Eng. 32(3), 588–601 (2020)

    Article  Google Scholar 

  35. Walsh, C.G., Ribeiro, J.D., Franklin, J.C.: Predicting risk of suicide attempts over time through machine learning. Clin. Psychol. Sci. 5(3), 457–469 (2017)

    Article  Google Scholar 

  36. Walsh, C.G., Ribeiro, J.D., Franklin, J.C.: Predicting suicide attempts in adolescents with longitudinal clinical data and machine learning. J. Child Psychol. Psychiatry 59(12), 1261–1270 (2018)

    Article  Google Scholar 

  37. World Health Organization: Other common mental disorders: Global health estimates. Geneva: World Health Organization, pp. 1–24 (2017)

    Google Scholar 

  38. Zhu, J., Zou, H., Rosset, S., Hastie, T.: Multi-class adaboost. Stat. Interface 2, 349–360 (2009)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgement

The first author would like to acknowledge financial support from a Research and Innovation Summer Research Internship Program scholarship awarded by the University of Newcastle, Australia.

Author information

Authors and Affiliations

  1. School of Electrical Engineering and Computing, The University of Newcastle, Callaghan, NSW, 2308, Australia

    Ben Sutter, Raymond Chiong, Gregorius Satia Budhi & Sandeep Dhakal

  2. Informatics Department, Petra Christian University, Surabaya, 60236, Indonesia

    Gregorius Satia Budhi

Authors
  1. Ben Sutter
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Raymond Chiong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gregorius Satia Budhi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sandeep Dhakal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Raymond Chiong .

Editor information

Editors and Affiliations

  1. i-SOMET Incorporate Association, Morioka, Japan

    Hamido Fujita

  2. Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia

    Ali Selamat

  3. Western Norway University of Applied Sciences, Bergen, Norway

    Jerry Chun-Wei Lin

  4. Texas State University San Marcos, San Marcos, TX, USA

    Moonis Ali

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sutter, B., Chiong, R., Budhi, G.S., Dhakal, S. (2021). Predicting Psychological Distress from Ecological Factors: A Machine Learning Approach. In: Fujita, H., Selamat, A., Lin, J.CW., Ali, M. (eds) Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices. IEA/AIE 2021. Lecture Notes in Computer Science(), vol 12798. Springer, Cham. https://doi.org/10.1007/978-3-030-79457-6_30

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-79457-6_30

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-79456-9

  • Online ISBN: 978-3-030-79457-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation