Predicting anxiety and depression in elderly patients using machine learning technology

Arkaprabha Sau; Ishita Bhakta

Predicting anxiety and depression in elderly patients using machine learning technology

View Fulltext

Author(s): Arkaprabha Sau¹ and Ishita Bhakta²
- Affiliations: 1: Department of Community Medicine , R.G. Kar Medical College and Hospital , Kolkata 700 004 , India ;
  2: Department of Computer Science and Engineering , West Bengal University of Technology , Kolkata 700064 , India
Source: Volume 4, Issue 6, December 2017, p. 238 – 243
DOI: 10.1049/htl.2016.0096 , Online ISSN 2053-3713

This is an open access article published by the IET under the Creative Commons Attribution -NonCommercial License (http://creativecommons.org/licenses/by-nc/3.0/)

Received 17/11/2016, Accepted 19/07/2017, Revised 15/07/2017, Published 10/11/2017

Anxiety and depression are two important mental health problems among the geriatric population. They are often undiagnosed and directly or indirectly responsible for various morbidities. Early and timely diagnosis has immense effect on appropriate management of anxiety and depression along with its co-morbidities. Owing to time constraint and enormous patient load, especially in developing county such as India it is hardly possible for a physician or surgeon to identify a geriatric patient suffering from anxiety and depression using any psychometric analysis tool. So, it is of utmost importance to develop a predictive model for automated diagnosis of anxiety and depression among them. This Letter aims to develop an appropriate predictive model, to diagnose anxiety and depression among older patient from socio-demographic and health-related factors, using machine learning technology. Ten classifiers were evaluated with a data set of 510 geriatric patients and tested with ten-fold cross-validation method. Highest prediction accuracy of 89% was obtained with random forest (RF) classifier. This RF model was tested with another data set from separate 110 older patients for its external validity. Its predictive accuracy was found to be 91% and false positive (FP) rate was 10%, compared with gold standard tool.

References

1. 1)
  - 3. Park, K.: ‘Park's textbook of preventive and social medicine’ (M/s Banarsidas Bhanot, Jabalpur, 2015, 23rd edn.).
2. 2)
  - 30. López, M., Ramírez, J., Górriz, J.M., et al: ‘Automatic tool for Alzheimer's disease diagnosis using PCA and Bayesian classification rules’, Electron. Lett., 2009, 45, (8), pp. 389–391 (doi: 10.1049/el.2009.0176).
3. 3)
  - 6. Gerrits, M.M.J.G., van Oppen, P., van Marwijk, H.W.J., et al: ‘Pain and the onset of depressive and anxiety disorders’, PAIN®, 2014, 155, (1), pp. 53–59 (doi: 10.1016/j.pain.2013.09.005).
4. 4)
  - 28. Karanasiou, G.S., Tripoliti, E.E., Papadopoulos, T.G., et al: ‘Predicting adherence of patients with HF through machine learning techniques’, Healthc. Technol. Lett., 2016, 3, (3), pp. 165–170 (doi: 10.1049/htl.2016.0041).
5. 5)
  - 26. Holte, R.C.: ‘Very simple classification rules perform well on most commonly used datasets’, Mach. Learn., 1993, 11, (1), pp. 63–90 (doi: 10.1023/A:1022631118932).
6. 6)
  - 37. Suhasini, A., Palanivel, S., Ramalingam, V.: ‘Multimodel decision support system for psychiatry problem’, Expert Syst. Appl., 2011, 38, (5), pp. 4990–4997 (doi: 10.1016/j.eswa.2010.09.152).
7. 7)
  - 10. Mathur, R., Pérez-Pinar, M., Foguet-Boreu, Q., et al: ‘Risk of incident cardiovascular events amongst individuals with anxiety and depression: a prospective cohort study in the East London primary care database’, J. Affect. Disord., 2016, 206, pp. 41–47 (doi: 10.1016/j.jad.2016.07.046).
8. 8)
  - 20. Cole, M.G., Dendukuri, N.: ‘Risk factors for depression among elderly community subjects: a systematic review and meta-analysis’, Am. J. Psychiatry, 2003, 160, (6), pp. 1147–1156 (doi: 10.1176/appi.ajp.160.6.1147).
9. 9)
  - 18. Snaith, R.P.: ‘The hospital anxiety and depression scale’, Health Qual. Life Outcomes, 2003, 1, p. 29 (doi: 10.1186/1477-7525-1-29).
10. 10)
  - 5. Mental health and older adults. Available at: http://www.who.int/mediacentre/factsheets /fs381/en/, accessed 12 November 2016.
11. 11)
  - 35. Lashkari, A., Pak, F., Firouzmand, M.: ‘Breast thermal images classification using optimal feature selectors and classifiers’, J. Eng., 2016, 1, (1), pp. 1–12.
12. 12)
  - 8. Doyle, T., Palmer, S., Johnson, J., et al: ‘Association of anxiety and depression with pulmonary-specific symptoms in chronic obstructive pulmonary disease’, Int. J. Psychiatry Med., 2013, 45, (2), pp. 189–202 (doi: 10.2190/PM.45.2.g).
13. 13)
  - 4. Health and Age-Related Changes. Available at: http://www.apa.org/pi/aging/resources/guides/ older.aspx, accessed 10 November 2016.
14. 14)
  - 34. Ferreira, E.J., Pereira, R.C.T., Delbem, A.C.B., et al: ‘Random subspace method for analysing coffee with electronic tongue’, Electron. Lett., 2007, 43, (21), pp. 1138–1140 (doi: 10.1049/el:20071182).
15. 15)
  - 15. WHO: ‘World Health Day’ (WHO, 2017). Available at: http://www.who.int/campaigns/world-health-day/2017/en/, accessed 12 November 2016.
16. 16)
  - 25. Hall, M.A., Smith, L.A.: ‘Practical feature subset selection for machine learning’, Computer Science ’98 Proceedings of the 21st Australasian Computer Science Conference ACSC’98, Perth, Berlin: Springer, 4–6 February1998, 20, (1), pp. 181–191..
17. 17)
  - 31. Orlandi, S., Reyes Garcia, C.A., Bandini, A., et al: ‘Application of pattern recognition techniques to the classification of full-term and preterm infant cry’, J. Voice, 2016, 30, (6), pp. 656–663 (doi: 10.1016/j.jvoice.2015.08.007).
18. 18)
  - 14. Pilania, M., Bairwa, M., Kumar, N., et al: ‘Elderly depression in India: an emerging public health challenge’, Australas. Med. J., 2013, 6, (3), pp. 107–111 (doi: 10.4066/AMJ.2013.1583).
19. 19)
  - 13. Djernes, J.K.: ‘Prevalence and predictors of depression in populations of elderly: a review’, Acta Psychiatr. Scand., 2006, 113, (5), pp. 372–387 (doi: 10.1111/j.1600-0447.2006.00770.x).
20. 20)
  - 27. Xiong, X., Kim, Y., Baek, Y., et al: ‘Analysis of breast cancer using data mining statistical techniques’. Int. Conf. Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, 2005, pp. 82–87.
21. 21)
  - 21. Vink, D., Aartsen, M.J., Schoevers, R.A.: ‘Risk factors for anxiety and depression in the elderly: a review’, J. Affect. Disord., 2008, 106, (1-2), pp. 29–44 (doi: 10.1016/j.jad.2007.06.005).
22. 22)
  - 1. World Population Ageing. United Nations, 2015. Available at: http://www.un.org/en/development/desa/population/publications/pdf/ageing/WPA2015_Report.pdf, accessed 11 November 2016.
23. 23)
  - 11. Watkins, L.L., Koch, G.G., Sherwood, A., et al: ‘Association of anxiety and depression with all-cause mortality in individuals with coronary heart disease’, J. Am. Heart Assoc., 2013, 2, (2), p. e000068.
24. 24)
  - 23. Bjelland, I., Dahl, A.A., Haug, T.T., et al: ‘The validity of the hospital anxiety and depression scale: an updated literature review’, J. Psychosom. Res., 2002, 52, (2), pp. 69–77 (doi: 10.1016/S0022-3999(01)00296-3).
25. 25)
  - 33. Beura, S., Majhi, B., Dash, R., et al: ‘Classification of mammogram using two-dimensional discrete orthonormal S-transform for breast cancer detection’, Healthc. Technol. Lett., 2015, 2, (2), pp. 46–51 (doi: 10.1049/htl.2014.0108).
26. 26)
  - 2. Population Data. Available at http://data.worldbank.org/indicator/SP.POP.TOTL, accessed 11 November 2016.
27. 27)
  - 36. Painuli, S., Elangovan, M., Sugumaran, V.: ‘Tool condition monitoring using K-star algorithm’, Expert Syst. Appl., 2014, 41, (6), pp. 2638–2643 (doi: 10.1016/j.eswa.2013.11.005).
28. 28)
  - 38. Bhakta, I, Sau, A.: ‘Prediction of depression among senior citizens using machine learning classifiers’, Int. J. Comput. Appl., 2016, 144, (7), pp. 11–16.
29. 29)
  - 7. Kabra, N., Nadkarni, A.: ‘Prevalence of depression and anxiety in irritable bowel syndrome: a clinic based study from India’, Indian J. Psychiatry, 2013, 55, (1), p. 77 (doi: 10.4103/0019-5545.105520).
30. 30)
  - 19. Sagna, A., Gallo, J.J., Pontone, G.M.: ‘Systematic review of factors associated with depression and anxiety disorders among older adults with Parkinson's disease’, Parkinsonism Relat. Disord., 2014, 20, (7), pp. 708–715 (doi: 10.1016/j.parkreldis.2014.03.020).
31. 31)
  - 29. Melillo, P., Jovic, A., Luca, N.D., et al: ‘Automatic classifier based on heart rate variability to identify fallers among hypertensive subjects’, Healthc. Technol. Lett., 2015, 2, (4), pp. 89–94 (doi: 10.1049/htl.2015.0012).
32. 32)
  - 32. Ansari, S., Shafi, I., Ahmad, J., et al: ‘Neural network-based approach for the non-invasive diagnosis and classification of hepatotropic viral disease’, IET Commun., 2012, 6, (18), pp. 3265–3273 (doi: 10.1049/iet-com.2011.0186).
33. 33)
  - 22. Zigmond, A.S., Snaith, R.P.: ‘The hospital anxiety and depression scale’, Acta Psychiatr. Scand., 1983, 67, (6), pp. 361–370 (doi: 10.1111/j.1600-0447.1983.tb09716.x).
34. 34)
  - 16. Evans, M., Mottram, P.: ‘Diagnosis of depression in elderly patients’, Adv. Psychiatr. Treat., 2000, 6, (1), pp. 49–56 (doi: 10.1192/apt.6.1.49).
35. 35)
  - 9. Strine, T.W., Mokdad, A.H., Balluz, L.S., et al: ‘Depression and anxiety in the United States: findings from the 2006 behavioral risk factor surveillance system’, Psychiatr. Serv., 2008, 59, (12), pp. 1383–1390 (doi: 10.1176/ps.2008.59.12.1383).
36. 36)
  - 12. ‘WHO|Depression’, WHO. Available at: http://www.who.int/mediacentre/factsheets/fs369/en/, accessed 12 November 2016.
37. 37)
  - 24. Flint, A.J., Rifat, S.L.: ‘Factor structure of the hospital anxiety and depression scale in older patients with major depression’, Int. J. Geriatr. Psychiatry, 2002, 17, (2), pp. 117–123 (doi: 10.1002/gps.535).

Login

Not registered yet?

Share

Tools

Login to add to favourites

Key

Predicting anxiety and depression in elderly patients using machine learning technology

References

Related content