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An early assessment of Persistent Depression Disorder using machine learning algorithm

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Abstract

Although various algorithms and strategies have been proposed for predicting depression and anxiety, none of the work is still suggested for an automated system for an early assessment of Dysthymia. This study aimed to enhance the accuracy of early diagnosis for Persistent Depression Disorder (PDD) through an improved machine learning technique utilizing the stacking SVM ensemble approach. To expedite the initial screening of dysthymia in students, a quantitative analysis of behavioral data based on machine learning was employed. The research collected behavioral data from 137 college students, and the gathered data was used for model development and experimentation. The findings revealed that PDD was predominantly prevalent among middle-class undergraduates majoring in non-technical fields. Notably, PDD rates were higher among rural undergraduates from both high- and low-income backgrounds. The proposed stacked SVM model demonstrated superior performance, achieving an accuracy of 89.4%. Detecting PDD early among undergraduates is crucial for mental health professionals, and the stacked SVM method proved effective in this aspect.

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Data will be available from the corresponding author upon reasonable request.

References

  1. Sansone RA, Sansone LA (2009) Dysthymic disorder forlorn and overlooked? Psychiatry (Edgmont) 6(5):46–51

    Google Scholar 

  2. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2719439/pdf/PE_6_05_46.pdf. Accessed 3 Apr 2022

  3. http://www.allaboutdepression.com/dia_04.html. Accessed 1 Mar 2022

  4. Friston KJ, Frith CD, Dolan RJ, Price CJ, Zeki S, Ashburner JT, Penny WD (2004) Human brain function. Elsevier

    Google Scholar 

  5. Carter R (2014) The brain book. Dorling Kindersley Ltd

    Google Scholar 

  6. Stewart JL, Bismark AW, Towers DN, Coan JA, Allen JJB (2014) Resting frontal eeg asymmetry as an endophenotype for depression risk: sex specific patterns of frontal brain asymmetry. Psychophysiology 51(5):446–455

    Article  Google Scholar 

  7. Knott V, Mahoney C, Kennedy SH, Evans K (2001) EEG power, frequency, asymmetry and coherence in male depression. Psychiatry Res 106(2):123–140

    Article  Google Scholar 

  8. Puthankattil SD, Joseph PK (2012) Classification of eeg signals in normal and depression conditions by ann using rwe and signal entropyy. J Mech Med Biol 12(4):1240019

    Article  Google Scholar 

  9. Ahmadlou M, Adeli H, Adeli A (2013) Spatiotemporal analysis of relative convergence of EEGs reveals differences between brain dynamics of depressive women and men. EEG Clin Neurosci 44(3):175–181

    Article  Google Scholar 

  10. Hosseinifarda B, Moradi MH, Rostami R (2013) Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from eeg signal. Comput Methods Programs Biomed 109(2013):339–345

    Article  Google Scholar 

  11. Faust O, Ang PAC, Puthankattil SD, Joseph PK (2014) Depression diagnosis support system based on eeg signal entropies. J Mech Med Biol 14(3):1450035

    Article  Google Scholar 

  12. Kiss G, Vicsi K (2017) Comparison of read and spontaneous speech in case of automatic detection of depression. In: 8th IEEE International Conference on Cognitive Infocommunications (CogInfoCom), Debrecen, Hungary

  13. Morales MR, Levitan R (2016) Speech vs. text: A comparative analysis of features for depression detection systems. In: Spoken Language Technology Workshop (SLT), IEEE, San Diego, CA, USA

  14. Mantri S, Agrawal P, Dorle SS, Patil D, Wadhai VM (2013) Clinical depression analysis using speech features. In: Emerging Trends in Engineering and Technology (ICETET), 6th International Conference on, Nagpur, India

  15. Yang Y, Fairbairn C, Cohn JF (2013) Detecting depression severity from vocal prosody. IEEE Trans Affect Comput 4(2):142–150

    Article  Google Scholar 

  16. Cummins N, Scherer S, Krajewski J, Epps SSJ, Quatieri TF (2015) A review of depression and suicide risk assessment using speech analysis. Speech Commun 71:10–49

    Article  Google Scholar 

  17. Suslow T, Junghanns K, Arolt V (2001) Detection of facial expressions of emotions in depression. Percept Mot Skills 92(3):857–868

    Article  Google Scholar 

  18. Cohn JF, Kruez TS, Matthews I, Yang Y, Nguyen MH, Padilla MT, Zhou F, la Torre FD (2009) Detecting depression from facial actions and vocal prosody. In: 3rd International Conference on Affective Computing and Intelligent Interaction and Workshop, Amsterdam, Netherlands

  19. Meftah T, Thanh NL, Amar CB (2012) Detecting depression using multimodal approach of emotion recognition. In: International Conference on Complex Systems (ICCS), Agadir, Morocco

  20. Sau A, Bhakta I (2017) Predicting anxiety and depression in elderly patients using machine learning technology. Healthc Technol Lett 4(6):238–43

    Article  Google Scholar 

  21. Choudhury D, Gamon M, Counts S, Horvitz E (2013) Predicting depression via social media. ICWSM 13:1–10

    Google Scholar 

  22. Na KS, Cho SE, Geem ZW, Kim YK (2020) Predicting future onset of depression among community dwelling adults in the Republic of Korea using a machine learning algorithm. Neurosci Lett 721:134804. https://doi.org/10.1016/j.neulet.2020.134804. (ISSN 0304-3940)

    Article  Google Scholar 

  23. Mason BJ, Kocsis JH, Leon AC (1993) Measurement of severity and treatment response in dysthymia. Psychiatric Ann 23:625–631. https://doi.org/10.3928/0048-5713-19931101-09

    Article  Google Scholar 

  24. Hamilton M (1960) A rating scale for depression. J Neurol Neurosurg Psychiatry 23:56–62

    Article  Google Scholar 

  25. Lam R, Michalak E, Swinson R (2006) Assessment scales in depression, mania and anxiety. Taylor and Francis, London. http://files3.moshaverfa.com/en/pdf/Assessment/2005-Assessment_scales_in_depression,_mania_and_anxietyLam,Michalak,Swinson.pdf. Accessed 11 Mar 2022

    Google Scholar 

  26. Clinical depression, DepressionFree.com. http://www.depressionfree.com/clinical.html. Accessed 11 Mar 2022

  27. Non-clinical depression, DepressionFree.com. http://www.depressionfree.com/non-clinical.html. Accessed 11 Mar 2022

  28. Classification - Machine Learning. https://www.simplilearn.com/classification-machine-learning-tutorial. Accessed 21 Jan 2023

  29. Mohapatra S (2008) Development of impulse noise detection schemes for selective filtering, Master’s thesis. National Institute of Technology Rourkela

  30. Salman AO, Geman O (2023) Evaluating three machine learning classification methods for effective COVID-19 diagnosis. Int J Math Stat Comput Sci 1:1–14. https://doi.org/10.59543/ijmscs.v1i.7693

    Article  Google Scholar 

  31. Upadhyay DK, Mohapatra S, Singh NK (2023) Heterogeneous Bootstrapped Ensemble Model for an Early Assessment of MDD. J Harbin Eng Univ (ISSN: 1006–7043) 44(8)

  32. Upadhyay DK, Mohapatra S, Singh NK (2023) Assessment of Generalized Anxiety Disorder and Mood Disorder in Undergraduate Students During the Coronavirus Disease (COVID-19) Pandemic. Computational Intelligence in Analytics and Information Systems (CIAIS)Volume: Volume 1: Data Science and AI, Selected Papers from CIAIS-2021, 183

  33. Alickovic E, Subasi A (2018) Ensemble SVM method for automatic sleep stage classification. IEEE Trans Instrum Meas 67(6):1258–1265

    Article  Google Scholar 

  34. Chen Z, Jiang F, Cheng Y, Gu X, Liu W, Peng J (2018) XGBoost classifier for DDoS attack detection and analysis in SDN-based cloud. IEEE Int Conf Big Data Smart Comput (BigComp) 2018:251–256. https://doi.org/10.1109/BigComp.2018.00044

    Article  Google Scholar 

  35. Cvetković J (2017) Breast cancer patients’ depression prediction by machine learning approach. Cancer Invest. https://doi.org/10.1080/07357907.2017.1363892

    Article  Google Scholar 

  36. Krishni (2018) K-fold cross validation. Available at: https://medium.com/datadriveninvestor/k-fold-cross-validation-6b8518070833. Accessed 23 Jan 2023

  37. Yadav A, Vishwakarma D (2020) A Multilingual Framework of CNN and Bi-LSTM for Emotion Classification”,. Available: https://doi.org/10.1109/ICCCNT49239.2020.9225614

  38. Brown AM (2005) A new software for carrying out one-way ANOVA post hoc tests. Comput Methods Programs Biomed 79(1):89–95. https://doi.org/10.1016/j.cmpb.2005.02.007. (ISSN 0169-2607)

    Article  Google Scholar 

  39. Choudhury A, Khan MRH, Nahim NZ, Tulon SR, Islam S, Chakrabarty A (2019) Predicting Depression in Bangladeshi Undergraduates using Machine Learning. 2019 IEEE Region 10 Symposium (TENSYMP), pp 789–794. https://doi.org/10.1109/TENSYMP46218.2019.8971369

  40. Goyal S, Bhatia PK (2022) Heterogeneous stacked ensemble classifier for software defect prediction. Multimed Tools Appl 81:37033–37055. https://doi.org/10.1007/s11042-021-11488-6

    Article  Google Scholar 

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The authors declared that this study has received no financial support.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Birla Institute of Technology Mesra, Ranchi, Jharkhand, India

    Devesh Kumar Upadhyay, Subrajeet Mohapatra & Niraj Kumar Singh

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  1. Devesh Kumar Upadhyay
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  2. Subrajeet Mohapatra
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  3. Niraj Kumar Singh
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Correspondence to Devesh Kumar Upadhyay.

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Upadhyay, D.K., Mohapatra, S. & Singh, N.K. An early assessment of Persistent Depression Disorder using machine learning algorithm. Multimed Tools Appl 83, 49149–49171 (2024). https://doi.org/10.1007/s11042-023-17369-4

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