CFP last date
22 July 2024
Reseach Article

Predicting Depression using a Biochemistry Profile and Machine Learning for Better Risk Stratification

by Tauseef Ibne Mamun, Lamia Alam
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 51
Year of Publication: 2022
Authors: Tauseef Ibne Mamun, Lamia Alam
10.5120/ijca2022921924

Tauseef Ibne Mamun, Lamia Alam . Predicting Depression using a Biochemistry Profile and Machine Learning for Better Risk Stratification. International Journal of Computer Applications. 183, 51 ( Feb 2022), 27-32. DOI=10.5120/ijca2022921924

@article{ 10.5120/ijca2022921924,
author = { Tauseef Ibne Mamun, Lamia Alam },
title = { Predicting Depression using a Biochemistry Profile and Machine Learning for Better Risk Stratification },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2022 },
volume = { 183 },
number = { 51 },
month = { Feb },
year = { 2022 },
issn = { 0975-8887 },
pages = { 27-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number51/32274-2022921924/ },
doi = { 10.5120/ijca2022921924 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:15:30.683272+05:30
%A Tauseef Ibne Mamun
%A Lamia Alam
%T Predicting Depression using a Biochemistry Profile and Machine Learning for Better Risk Stratification
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 51
%P 27-32
%D 2022
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Depression is one of the mental health issues that are responsible for various morbidities if it remains undiagnosed. Previous works that used machine learning to predict depression used mainly qualitative information like socio-economic or socio-demographic data for creating predictive models. But there is a chance of getting biased qualitative data from patients that will hinder any correct prediction. Our goal was to examine if the major form of depression can be predicted with a high accuracy using a patient’s biochemistry profile using Machine Learning. We also examined if minor depression can be predicted for a large portion of patients. The result suggests the method we proposed can be used as the first point of screening for depression especially major depression.

References
  1. H. Ritchie and M. Roser, “Mental Health,” Our World Data, Jan. 2018, Accessed: Feb. 15, 2021. [Online]. Available: https://ourworldindata.org/mental-health.
  2. P. E. Greenberg, A.-A. Fournier, T. Sisitsky, C. T. Pike, and R. C. Kessler, “The economic burden of adults with major depressive disorder in the United States (2005 and 2010),” J. Clin. Psychiatry, vol. 76, no. 2, pp. 155–162, 2015.
  3. L. L. Judd, M. P. Paulus, K. B. Wells, and M. H. Rapaport, “Socioeconomic burden of subsyndromal depressive symptoms and major depression in a sample of the general population.,” 1996.
  4. D. A. Regier, W. E. Narrow, D. S. Rae, R. W. Manderscheid, B. Z. Locke, and F. K. Goodwin, “The de facto US mental and addictive disorders service system: Epidemiologic Catchment Area prospective 1-year prevalence rates of disorders and services,” Arch. Gen. Psychiatry, vol. 50, no. 2, pp. 85–94, 1993.
  5. J. C. Coyne, S. Fechner-Bates, and T. L. Schwenk, “Prevalence, nature, and comorbidity of depressive disorders in primary care,” Gen. Hosp. Psychiatry, vol. 16, no. 4, pp. 267–276, 1994.
  6. A. Halfin, “Depression: the benefits of early and appropriate treatment,” Am. J. Manag. Care, vol. 13, no. 4, p. S92, 2007.
  7. A. Lasalviaet al., “Global pattern of experienced and anticipated discrimination reported by people with major depressive disorder: a cross-sectional survey,” The Lancet, vol. 381, no. 9860, pp. 55–62, 2013.
  8. K. B. Tharaldsen, P. Stallard, P. Cuijpers, E. Bru, and J. F. Bjaastad, “‘It’sa bit taboo’: a qualitative study of Norwegian adolescents’ perceptions of mental healthcare services,” Emot. Behav. Difficulties, vol. 22, no. 2, pp. 111–126, 2017.
  9. R. A. Miech and M. J. Shanahan, “Socioeconomic status and depression over the life course,” J. Health Soc. Behav., pp. 162–176, 2000.
  10. B. G. Link, M. C. Lennon, and B. P. Dohrenwend, “Socioeconomic status and depression: The role of occupations involving direction, control, and planning,” Am. J. Sociol., vol. 98, no. 6, pp. 1351–1387, 1993.
  11. N. Akhtar-Danesh and J. Landeen, “Relation between depression and sociodemographic factors,” Int. J. Ment. Health Syst., vol. 1, no. 1, pp. 1–9, 2007.
  12. A. Barua, M. K. Ghosh, N. Kar, and M. A. Basilio, “Socio-demographic factors of geriatric depression,” Indian J. Psychol. Med., vol. 32, no. 2, pp. 87–92, 2010.
  13. E. M. de Souza Filho et al., “Can machine learning be useful as a screening tool for depression in primary care?,” J. Psychiatr. Res., vol. 132, pp. 1–6, Jan. 2021, doi: 10.1016/j.jpsychires.2020.09.025.
  14. A. Sau and I. Bhakta, “Predicting anxiety and depression in elderly patients using machine learning technology,” Healthc. Technol. Lett., vol. 4, no. 6, pp. 238–243, 2017.
  15. A. Priya, S. Garg, and N. P. Tigga, “Predicting anxiety, depression and stress in modern life using machine learning algorithms,” Procedia Comput. Sci., vol. 167, pp. 1258–1267, 2020.
  16. M. R. Islam, M. A. Kabir, A. Ahmed, A. R. M. Kamal, H. Wang, and A. Ulhaq, “Depression detection from social network data using machine learning techniques,” Health Inf. Sci. Syst., vol. 6, no. 1, pp. 1–12, 2018.
  17. M. Trotzek, S. Koitka, and C. M. Friedrich, “Utilizing neural networks and linguistic metadata for early detection of depression indications in text sequences,” IEEE Trans. Knowl. Data Eng., vol. 32, no. 3, pp. 588–601, 2018.
  18. N. Schwarz, “Self-reports: how the questions shape the answers.,” Am. Psychol., vol. 54, no. 2, p. 93, 1999.
  19. T. L. Jones, M. A. J. Baxter, and V. Khanduja, “A quick guide to survey research,” Ann. R. Coll. Surg. Engl., vol. 95, no. 1, pp. 5–7, 2013.
  20. L. Alam, “Investigating the Impact of Explanation on Repairing Trust in Ai Diagnostic Systems for Re-Diagnosis,” 2020.
  21. Mueller, S.T., Veinott, E.S., Hoffman, R.R., Klein, G., Alam, L., Mamun, T. and Clancey, W.J., "Principles of Explanation in Human-AI Systems." arXiv preprint arXiv:2102.04972, 2021.
  22. “What Is the Chemistry Behind Depression?,” Verywell Mind. https://www.verywellmind.com/the-chemistry-of-depression-1065137 (accessed Feb. 15, 2021).
  23. Salvadore, G., van der Veen, J.W., Zhang, Y., Marenco, S., Machado-Vieira, R., Baumann, J., Ibrahim, L.A., Luckenbaugh, D.A., Shen, J., Drevets, W.C. and Zarate Jr, C.A., “An investigation of amino-acid neurotransmitters as potential predictors of clinical improvement to ketamine in depression,” Int. J. Neuropsychopharmacol., vol. 15, no. 8, pp. 1063–1072, 2012.
  24. M.-L. Derom, C. Sayón-Orea, J. M. Martínez-Ortega, and M. A. Martínez-González, “Magnesium and depression: a systematic review,” Nutr. Neurosci., vol. 16, no. 5, pp. 191–206, Sep. 2013, doi: 10.1179/1476830512Y.0000000044.
  25. Widmer, J., Mouthon, D., Raffin, Y., Chollet, D., Hilleret, H., Malafosse, A., &Bovier, P.,“Weak Association between Blood Sodium, Potassium, and Calcium and Intensity of Symptoms in Major Depressed Patients,” Neuropsychobiology, vol. 36, no. 4, pp. 164–171, 1997, doi: 10.1159/000119378.
  26. Y.-F. Peng, Y. Xiang, and Y.-S. Wei, “The significance of routine biochemical markers in patients with major depressive disorder,” Sci. Rep., vol. 6, no. 1, Art. no. 1, Sep. 2016, doi: 10.1038/srep34402.
  27. A. Parekh, D. Smeeth, Y. Milner, and S. Thuret, “The role of lipid biomarkers in major depression,” in Healthcare, 2017, vol. 5, no. 1, p. 5.
  28. D. Rafter, “Biochemical markers of anxiety and depression,” Psychiatry Res., vol. 103, no. 1, pp. 93–96, Aug. 2001, doi: 10.1016/S0165-1781(01)00251-7.
  29. Glueck, C. J., Tieger, M., Kunkel, R., Tracy, T., Speirs, J., Streicher, P., &Illig, E., “Improvement in symptoms of depression and in an index of life stressors accompany treatment of severe hypertriglyceridemia,” Biol. Psychiatry, vol. 34, no. 4, pp. 240–252, 1993.
  30. B. M. Ross, P. Ward, and I. Glen, “Delayed vasodilatory response to methylnicotinate in patients with unipolar depressive disorder,” J. Affect. Disord., vol. 82, no. 2, pp. 285–290, 2004.
  31. Maes, M., Vandoolaeghe, E., Neels, H., Demedts, P., Wauters, A., Meltzer, H. Y., Altamura, C. andDesnyder, R., “Lower serum zinc in major depression is a sensitive marker of treatment resistance and of the immune/inflammatory response in that illness,” Biol. Psychiatry, vol. 42, no. 5, pp. 349–358, 1997.
  32. A.-M. Hvas, S. Juul, P. Bech, and E. Nexø, “Vitamin B6 level is associated with symptoms of depression,” Psychother. Psychosom., vol. 73, no. 6, pp. 340–343, 2004.
  33. Y. W.-Y. Yu, T.-J. Chen, Y.-C. Wang, Y.-J. Liou, C.-J. Hong, and S.-J. Tsai, “Association analysis for neuronal nitric oxide synthase gene polymorphism with major depression and fluoxetine response,” Neuropsychobiology, vol. 47, no. 3, pp. 137–140, 2003.
  34. B. Nowok, G. M. Raab, and C. Dibben, “synthpop: Bespoke creation of synthetic data in R,” J. Stat. Softw., vol. 74, no. 11, pp. 1–26, 2016.
  35. https://wwwn.cdc.gov/Nchs/Nhanes/2017-2018/BIOPRO_J.htm#Component_Description (accessed Feb. 16, 2021).
  36. J. Istvan, K. Zavela, and G. Weidner, “Body weight and psychological distress in NHANES I.,” Int. J. Obes. Relat. Metab. Disord. J. Int. Assoc. Study Obes., vol. 16, no. 12, pp. 999–1003, 1992.
  37. Jia, Z., Li, X., Yuan, X., Zhang, B., Liu, Y., Zhao, J., and Li, S., “Depression is associated with diabetes status of family members: NHANES (1999–2016),” J. Affect. Disord., vol. 249, pp. 121–126, 2019.
  38. R. S. Shim, P. Baltrus, J. Ye, and G. Rust, “Prevalence, treatment, and control of depressive symptoms in the United States: results from the National Health and Nutrition Examination Survey (NHANES), 2005–2008,” J. Am. Board Fam. Med., vol. 24, no. 1, pp. 33–38, 2011.
  39. J. K. Vallance, E. A. Winkler, P. A. Gardiner, G. N. Healy, B. M. Lynch, and N. Owen, “Associations of objectively-assessed physical activity and sedentary time with depression: NHANES (2005–2006),” Prev. Med., vol. 53, no. 4–5, pp. 284–288, 2011.
Index Terms

Computer Science
Information Sciences

Keywords

Depression Early Detection Risk Stratification