Diagnosing Major Depressive Disorder using Activity Data from Wearable Sensors and Machine Learning
DOI:
https://doi.org/10.47611/jsrhs.v11i4.3684Keywords:
Machine Learning, Depression, Psychological Disorder, Artificial Intelligence, Neural NetworkAbstract
Major Depressive Disorder (MDD), a mood disorder, is the most common psychological disorder. MDD manifests itself through a range of deadly symptoms, while diagnosis remains difficult and costly, often requiring psychiatrists or specialized techniques. An easier, and possibly early, diagnosis could improve treatment and outcomes. To address this unmet need, we developed a novel machine learning algorithm to detect MDD based on an individual's activity data i.e. movement combined with light data. The dataset from Kaggle.com included activity data for fifty-five participants in 2021. Our algorithm determined that disturbances in activity is a symptom that can be used to predict Major Depressive Disorder. This insight has the potential to accurately detect and diagnose a person with MDD. In conclusion, the algorithm connecting activity to MDD paves the way to an easier and effective way of diagnosing depression.
Downloads
References or Bibliography
Actigraphy. Stanford Health Care (SHC) - Stanford Medical Center. (2017, September 12), from https://stanfordhealthcare.org/medical-tests/s/sleep-disorder-tests/procedures/actigraphy.html
American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). https://doi.org/10.1176/appi.books.9780890425596
Cho, C. H., Lee, T., Kim, M. G., In, H. P., Kim, L., & Lee, H. J. (2019). Mood Prediction of Patients With Mood Disorders by Machine Learning Using Passive Digital Phenotypes Based on the Circadian Rhythm: Prospective Observational Cohort Study. Journal of medical Internet research, 21(4), e11029. https://doi.org/10.2196/11029
Haque UM, Kabir E, Khanam R (2021) Detection of child depression using machine learning methods. PLOS ONE 16(12): e0261131. https://doi.org/10.1371/journal.pone.0261131
Kessler, R. C., Berglund, P., Demler, O., Jin, R., Koretz, D., Merikangas, K. R., Rush, A. J., Walters, E. E., & Wang, P. S. (2003). The Epidemiology of Major Depressive Disorder. JAMA, 289(23), 3095. https://doi.org/10.1001/jama.289.23.3095
Mayo Clinic Staff. (2021, October 29). Mood disorders. Mayo Clinic, from https://www.mayoclinic.org/diseases-conditions/mood-disorders/symptoms-causes/syc-20365057
Mental Health Disorder Statistics. (2019, November 19). Hopkins Medicine, from https://www.hopkinsmedicine.org/health/wellness-and-prevention/mental-health-disorder-statistics
Mobius. (2021). The Depression Dataset, Version 1, from https://www.kaggle.com/datasets/arashnic/the-depression-dataset
Oseh, S. (2021, August 3). Depression-dataset analysis and Machine Learning. Kaggle, from https://www.kaggle.com/code/samuelkali/depression-dataset-analysis-and-machine-learning/output
Quilty, L. C., Robinson, J. J., Rolland, J. P., Fruyt, F. D., Rouillon, F., & Bagby, R. M. (2013). The structure of the Montgomery-Åsberg depression rating scale over the course of treatment for depression. International journal of methods in psychiatric research, 22(3), 175–184. https://doi.org/10.1002/mpr.1388
Su, D., Zhang, X., He, K., & Chen, Y. (2021). Use of machine learning approach to predict depression in the elderly in China: A longitudinal study. Journal of affective disorders, 282, 289–298. https://doi.org/10.1016/j.jad.2020.12.160
UN health agency reports depression now “leading cause of disability worldwide.” (2017, February 23). UN News, from https://news.un.org/en/story/2017/02/552062-un-health-agency-reports-depression-now-leading-cause-disability-worldwide#:~:text=Depression%20is%20the%20leading%20cause,young%20people%20and%20the%20elderly.
Weiner, S. (2018, February 12). Addressing the escalating psychiatrist shortage. AAMC, from http://www.aamc.org/news-insights/addressing-escalating-psychiatrist-shortage
Weir, K. (2011, December). The exercise effect. American Psychological Association, from https://www.apa.org/monitor/2011/12/exercise
X, C. (2021, April 8). Depression and motor activity. Kaggle, from https://www.kaggle.com/code/docxian/depression-and-motor-activity/comments
Published
How to Cite
Issue
Section
Copyright (c) 2022 Shlok Desai; Brianna Marsh
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Copyright holder(s) granted JSR a perpetual, non-exclusive license to distriute & display this article.
