User Centric Social Opinion and Clinical Behavioural Model for Depression Detection

Ayodeji Olusegun Ibitoye; Rantiola Fidelix Famutimi; Dauda Odunayo Olanloye; Ehisuoria Akioyamen

doi:doi:10.11648/j.ijiis.20211004.15

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User Centric Social Opinion and Clinical Behavioural Model for Depression Detection

Ayodeji Olusegun Ibitoye, Rantiola Fidelix Famutimi, Dauda Odunayo Olanloye, Ehisuoria Akioyamen

Published in International Journal of Intelligent Information Systems (Volume 10, Issue 4)

Received: 28 July 2021 Accepted: 20 August 2021 Published: 31 August 2021

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Abstract

In more recent time, depression as a lingering mental illness as continued to affect the way people act, and behave consciously or otherwise. Though it remained an undiagnosed disease globally without prejudice to age, gender, color or race; a lot of people never know implicitly or explicitly when they are depressed until it begins to affect their health conditions. While depression can be deciphered through text analysis in opinion mining, oftentimes, changes in human body also provides a convincing status of a depressed individual. No doubt, each data source can independently predict human depression status; however, the exclusive mutual relationship between both data sources has not been studied for depression detection. Therefore, in identifying meaningful correlations between clinical and behavioural data, this research detected depression by analyzing and matching mined patterns in users’ behavioural opinion through tweets with trackable changes in clinical body vitals using wearable device for effective therapy in depressed patient management. Thus, by using a 5-fold cross validation on the clustered data, Random Forest ensemble model was used to build the Social-Health Depression Detection Model (SH2DM) after data preprocessing and optimal feature extraction. The dual data sourced user-centric model produced a better predictive result in accuracy, precision and recall values when compared and evaluated with single data depression detection instances of clinical and behavioural records.

Published in	International Journal of Intelligent Information Systems (Volume 10, Issue 4)
DOI	10.11648/j.ijiis.20211004.15
Page(s)	69-73
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Depression Detection, Text-Analysis, Opinion Mining, Social-Health, Wearables, Random Forest, Decision Support

References

[1]	Gitanjali S, Iachan R, Scheidt PC, Overpeck MD, Sun W, Giedd JN: Prevalence of and Risk Factors for Depressive Symptoms Among Young Adolescents. Arch Pediatr Adolesc Med 2004, 158: 760-65.
[2]	Maurer DM, Raymond TJ, Davis BN. Depression: Screening and diagnosis. Am Family Phys (2018) 98: 508–15.
[3]	Gelaye B, Williams MA, Lemma S, Deyessa N, Bahretibeb Y, Shibre T, Wondimagegn D, Lemenhe A, Fann JR, Vander Stoep A, et al. Validity of the Patient Health Questionnaire-9 for depression screening and diagnosis in East Africa. Psychiatry Res. 2013; 210 (2): 653–61.
[4]	Saad M., Ray L. B., Bujaki B., Parvaresh A., Palamarchuk I., De Koninck J., Douglass A., Lee E. K., Soucy L. J., Fogel S., Morin C. M., Bastien C., Merali Z., Robillard R. Using heart rate profiles during sleep as a biomarker of depression. BMC Psychiatr. 2019; 19: 168.
[5]	De Choudhury, M., Gamon, M., Counts, S. and Horvitz, E., 2013, June. Predicting depression via social media. In Seventh international AAAI conference on weblogs and social media.
[6]	Yazdavar, A. H., Al-Olimat, H. S., Banerjee, T., Thirunarayan, K., & Sheth, A. P. Analyzing clinical depressive symptoms in twitter (2016).
[7]	Mathers, C. D., & Loncar, D. (2006). Projections of global mortality and burden of disease from 2002 to 2030. PLoS medicine, 3 (11), e442.
[8]	"Media- DBSA Facts about Depression-, Secure2.convio.net” 1 January, 2019. [Online]. Available: https://secure2.convio.net/dabsa/site/SPageServer/;jsessionid=00000000.app20101a?NONCE_TOKEN=B2DB 3A070406934661E7561D. [Accessed 21 November 2019].
[9]	Timothy J. Legg, "The effects of depression on the body and physical health. [online]," Medical News Today, 1 January 2019. [Online]. Available: https://www.medicalnewstoday.com/articles/322395.ph p. [Accessed 21 November 2019].
[10]	Unützer J, Park M. Strategies to improve the management of depression in primary care. Prim Care. (2012) 39: 415–31. doi: 10.1016/j.pop.2012.03.010.
[11]	Lin EH, Katon WJ, Simon GE, Von Korff M, Bush TM, Walker EA, et al. Low-intensity treatment of depression in primary care: is it problematic? Gen Hosp Psychiatry. (2000) 22: 78–83. doi: 10.1016/S0163-8343(00)00054-2.
[12]	Halfin A. Depression: the benefits of early and appropriate treatment. Am J Manag Care 2007 Nov; 13 (4 Suppl): S92-S97.
[13]	Mazzetta I., Gentile P., Pessione M., Suppa A., Zampogna A., Bianchini E., Irrera F., Stand-alone wearable system for ubiquitous real-time monitoring of muscle activation potentials. Sensors. 2018; 18: 1748.
[14]	Nieto-Riveiro L., Groba B., Miranda M. C., Concheiro P., Pazos A., Pousada T., Pereira J. Technologies for participatory medicine and health promotion in the elderly population. Medicine (Baltim.) 2018.
[15]	Valenza G., Nardelli M., Lanatà A., Gentili C., Bertschy G., Paradiso R., Scilingo E. P. Wearable monitoring for mood recognition in bipolar disorder based on history-dependent long-term heart rate variability analysis. IEEE J. Biomed. Health Inf. 2014; 18: 1625–1635.
[16]	Rohani D. A., Faurholt-Jepsen M., Kessing L. V., Bardram J. E. Correlations between objective behavioural features collected from mobile and wearable devices and depressive mood symptoms in patients with affective disorders: systematic review. JMIR mHealth uHealth. 2018.
[17]	Lauren Kolodzey, Peter D Grantcharov, Homero Rivas, Marlies P Schijven, Teodor P Grantcharov, Wearable technology in the operating room: a systematic review on behalf of the Wearable Technology in Healthcare Society.
[18]	Park, G., et al. Automatic personality assessment through social media language. J. of Pers. and Soc. Psychol. 108 (6), 934 (2015).
[19]	Reece, A. G., et al. Forecasting the onset and course of mental illness with Twitter data. Sci. Rep. 7 (1), 13006 (2017).
[20]	Nadeem, M. Identifying depression on Twitter. Preprint at arXiv: 1607.07384 (2016).
[21]	Mowery, D., Bryan, C., & Conway, M. Feature studies to inform the classification of depressive symptoms from Twitter data for population health. Preprint at arXiv: 1701.08229 (2017).
[22]	Wang, X., Zhang, C., Ji, Y., Sun, L., Wu, L. & Bao, Z. (2013). A depression detection model based on sentiment analysis in micro-blog social network. In PacificAsia Conference on Knowledge Discovery and Data Mining (pp. 201-213). Springer, Berlin, Heidelberg.
[23]	De Choudhury, M., Counts, S. and Horvitz, E., 2013, May. Social media as a measurement tool of depression in populations. In Proceedings of the 5th Annual ACM Web Science Conference (pp. 47-56). ACM.

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APA Style

Ayodeji Olusegun Ibitoye, Rantiola Fidelix Famutimi, Dauda Odunayo Olanloye, Ehisuoria Akioyamen. (2021). User Centric Social Opinion and Clinical Behavioural Model for Depression Detection. International Journal of Intelligent Information Systems, 10(4), 69-73. https://doi.org/10.11648/j.ijiis.20211004.15

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ACS Style

Ayodeji Olusegun Ibitoye; Rantiola Fidelix Famutimi; Dauda Odunayo Olanloye; Ehisuoria Akioyamen. User Centric Social Opinion and Clinical Behavioural Model for Depression Detection. Int. J. Intell. Inf. Syst. 2021, 10(4), 69-73. doi: 10.11648/j.ijiis.20211004.15

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AMA Style

Ayodeji Olusegun Ibitoye, Rantiola Fidelix Famutimi, Dauda Odunayo Olanloye, Ehisuoria Akioyamen. User Centric Social Opinion and Clinical Behavioural Model for Depression Detection. Int J Intell Inf Syst. 2021;10(4):69-73. doi: 10.11648/j.ijiis.20211004.15

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@article{10.11648/j.ijiis.20211004.15,
  author = {Ayodeji Olusegun Ibitoye and Rantiola Fidelix Famutimi and Dauda Odunayo Olanloye and Ehisuoria Akioyamen},
  title = {User Centric Social Opinion and Clinical Behavioural Model for Depression Detection},
  journal = {International Journal of Intelligent Information Systems},
  volume = {10},
  number = {4},
  pages = {69-73},
  doi = {10.11648/j.ijiis.20211004.15},
  url = {https://doi.org/10.11648/j.ijiis.20211004.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20211004.15},
  abstract = {In more recent time, depression as a lingering mental illness as continued to affect the way people act, and behave consciously or otherwise. Though it remained an undiagnosed disease globally without prejudice to age, gender, color or race; a lot of people never know implicitly or explicitly when they are depressed until it begins to affect their health conditions. While depression can be deciphered through text analysis in opinion mining, oftentimes, changes in human body also provides a convincing status of a depressed individual. No doubt, each data source can independently predict human depression status; however, the exclusive mutual relationship between both data sources has not been studied for depression detection. Therefore, in identifying meaningful correlations between clinical and behavioural data, this research detected depression by analyzing and matching mined patterns in users’ behavioural opinion through tweets with trackable changes in clinical body vitals using wearable device for effective therapy in depressed patient management. Thus, by using a 5-fold cross validation on the clustered data, Random Forest ensemble model was used to build the Social-Health Depression Detection Model (SH2DM) after data preprocessing and optimal feature extraction. The dual data sourced user-centric model produced a better predictive result in accuracy, precision and recall values when compared and evaluated with single data depression detection instances of clinical and behavioural records.},
 year = {2021}
}

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TY  - JOUR
T1  - User Centric Social Opinion and Clinical Behavioural Model for Depression Detection
AU  - Ayodeji Olusegun Ibitoye
AU  - Rantiola Fidelix Famutimi
AU  - Dauda Odunayo Olanloye
AU  - Ehisuoria Akioyamen
Y1  - 2021/08/31
PY  - 2021
N1  - https://doi.org/10.11648/j.ijiis.20211004.15
DO  - 10.11648/j.ijiis.20211004.15
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 69
EP  - 73
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20211004.15
AB  - In more recent time, depression as a lingering mental illness as continued to affect the way people act, and behave consciously or otherwise. Though it remained an undiagnosed disease globally without prejudice to age, gender, color or race; a lot of people never know implicitly or explicitly when they are depressed until it begins to affect their health conditions. While depression can be deciphered through text analysis in opinion mining, oftentimes, changes in human body also provides a convincing status of a depressed individual. No doubt, each data source can independently predict human depression status; however, the exclusive mutual relationship between both data sources has not been studied for depression detection. Therefore, in identifying meaningful correlations between clinical and behavioural data, this research detected depression by analyzing and matching mined patterns in users’ behavioural opinion through tweets with trackable changes in clinical body vitals using wearable device for effective therapy in depressed patient management. Thus, by using a 5-fold cross validation on the clustered data, Random Forest ensemble model was used to build the Social-Health Depression Detection Model (SH2DM) after data preprocessing and optimal feature extraction. The dual data sourced user-centric model produced a better predictive result in accuracy, precision and recall values when compared and evaluated with single data depression detection instances of clinical and behavioural records.
VL  - 10
IS  - 4
ER  -

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Author Information

Ayodeji Olusegun Ibitoye

Computer Science Programme, Bowen University, Iwo, Nigeria
Rantiola Fidelix Famutimi

Computer Science Programme, Bowen University, Iwo, Nigeria
Dauda Odunayo Olanloye

Computer Science Programme, Bowen University, Iwo, Nigeria
Ehisuoria Akioyamen

Computer Science Programme, Bowen University, Iwo, Nigeria

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APA Style

Ayodeji Olusegun Ibitoye, Rantiola Fidelix Famutimi, Dauda Odunayo Olanloye, Ehisuoria Akioyamen. (2021). User Centric Social Opinion and Clinical Behavioural Model for Depression Detection. International Journal of Intelligent Information Systems, 10(4), 69-73. https://doi.org/10.11648/j.ijiis.20211004.15

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ACS Style

Ayodeji Olusegun Ibitoye; Rantiola Fidelix Famutimi; Dauda Odunayo Olanloye; Ehisuoria Akioyamen. User Centric Social Opinion and Clinical Behavioural Model for Depression Detection. Int. J. Intell. Inf. Syst. 2021, 10(4), 69-73. doi: 10.11648/j.ijiis.20211004.15

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AMA Style

Ayodeji Olusegun Ibitoye, Rantiola Fidelix Famutimi, Dauda Odunayo Olanloye, Ehisuoria Akioyamen. User Centric Social Opinion and Clinical Behavioural Model for Depression Detection. Int J Intell Inf Syst. 2021;10(4):69-73. doi: 10.11648/j.ijiis.20211004.15

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@article{10.11648/j.ijiis.20211004.15,
  author = {Ayodeji Olusegun Ibitoye and Rantiola Fidelix Famutimi and Dauda Odunayo Olanloye and Ehisuoria Akioyamen},
  title = {User Centric Social Opinion and Clinical Behavioural Model for Depression Detection},
  journal = {International Journal of Intelligent Information Systems},
  volume = {10},
  number = {4},
  pages = {69-73},
  doi = {10.11648/j.ijiis.20211004.15},
  url = {https://doi.org/10.11648/j.ijiis.20211004.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20211004.15},
  abstract = {In more recent time, depression as a lingering mental illness as continued to affect the way people act, and behave consciously or otherwise. Though it remained an undiagnosed disease globally without prejudice to age, gender, color or race; a lot of people never know implicitly or explicitly when they are depressed until it begins to affect their health conditions. While depression can be deciphered through text analysis in opinion mining, oftentimes, changes in human body also provides a convincing status of a depressed individual. No doubt, each data source can independently predict human depression status; however, the exclusive mutual relationship between both data sources has not been studied for depression detection. Therefore, in identifying meaningful correlations between clinical and behavioural data, this research detected depression by analyzing and matching mined patterns in users’ behavioural opinion through tweets with trackable changes in clinical body vitals using wearable device for effective therapy in depressed patient management. Thus, by using a 5-fold cross validation on the clustered data, Random Forest ensemble model was used to build the Social-Health Depression Detection Model (SH2DM) after data preprocessing and optimal feature extraction. The dual data sourced user-centric model produced a better predictive result in accuracy, precision and recall values when compared and evaluated with single data depression detection instances of clinical and behavioural records.},
 year = {2021}
}

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TY  - JOUR
T1  - User Centric Social Opinion and Clinical Behavioural Model for Depression Detection
AU  - Ayodeji Olusegun Ibitoye
AU  - Rantiola Fidelix Famutimi
AU  - Dauda Odunayo Olanloye
AU  - Ehisuoria Akioyamen
Y1  - 2021/08/31
PY  - 2021
N1  - https://doi.org/10.11648/j.ijiis.20211004.15
DO  - 10.11648/j.ijiis.20211004.15
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 69
EP  - 73
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20211004.15
AB  - In more recent time, depression as a lingering mental illness as continued to affect the way people act, and behave consciously or otherwise. Though it remained an undiagnosed disease globally without prejudice to age, gender, color or race; a lot of people never know implicitly or explicitly when they are depressed until it begins to affect their health conditions. While depression can be deciphered through text analysis in opinion mining, oftentimes, changes in human body also provides a convincing status of a depressed individual. No doubt, each data source can independently predict human depression status; however, the exclusive mutual relationship between both data sources has not been studied for depression detection. Therefore, in identifying meaningful correlations between clinical and behavioural data, this research detected depression by analyzing and matching mined patterns in users’ behavioural opinion through tweets with trackable changes in clinical body vitals using wearable device for effective therapy in depressed patient management. Thus, by using a 5-fold cross validation on the clustered data, Random Forest ensemble model was used to build the Social-Health Depression Detection Model (SH2DM) after data preprocessing and optimal feature extraction. The dual data sourced user-centric model produced a better predictive result in accuracy, precision and recall values when compared and evaluated with single data depression detection instances of clinical and behavioural records.
VL  - 10
IS  - 4
ER  -

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