{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T18:10:37Z","timestamp":1773771037105,"version":"3.50.1"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2023,10,27]],"date-time":"2023-10-27T00:00:00Z","timestamp":1698364800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,10,27]],"date-time":"2023-10-27T00:00:00Z","timestamp":1698364800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-023-17369-4","type":"journal-article","created":{"date-parts":[[2023,10,27]],"date-time":"2023-10-27T10:01:48Z","timestamp":1698400908000},"page":"49149-49171","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["An\u00a0early assessment of Persistent Depression Disorder using machine learning algorithm"],"prefix":"10.1007","volume":"83","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2399-1850","authenticated-orcid":false,"given":"Devesh Kumar","family":"Upadhyay","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Subrajeet","family":"Mohapatra","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Niraj Kumar","family":"Singh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,27]]},"reference":[{"issue":"5","key":"17369_CR1","first-page":"46","volume":"6","author":"RA Sansone","year":"2009","unstructured":"Sansone RA, Sansone LA (2009) Dysthymic disorder forlorn and overlooked? Psychiatry (Edgmont) 6(5):46\u201351","journal-title":"Psychiatry (Edgmont)"},{"key":"17369_CR2","unstructured":"https:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC2719439\/pdf\/PE_6_05_46.pdf. Accessed 3 Apr 2022"},{"key":"17369_CR3","unstructured":"http:\/\/www.allaboutdepression.com\/dia_04.html. Accessed 1 Mar 2022"},{"key":"17369_CR4","volume-title":"Human brain function","author":"KJ Friston","year":"2004","unstructured":"Friston KJ, Frith CD, Dolan RJ, Price CJ, Zeki S, Ashburner JT, Penny WD (2004) Human brain function. Elsevier"},{"key":"17369_CR5","volume-title":"The brain book","author":"R Carter","year":"2014","unstructured":"Carter R (2014) The brain book. Dorling Kindersley Ltd"},{"issue":"5","key":"17369_CR6","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1111\/psyp.12191","volume":"51","author":"JL Stewart","year":"2014","unstructured":"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\u2013455","journal-title":"Psychophysiology"},{"issue":"2","key":"17369_CR7","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/S0925-4927(00)00080-9","volume":"106","author":"V Knott","year":"2001","unstructured":"Knott V, Mahoney C, Kennedy SH, Evans K (2001) EEG power, frequency, asymmetry and coherence in male depression. Psychiatry Res 106(2):123\u2013140","journal-title":"Psychiatry Res"},{"issue":"4","key":"17369_CR8","doi-asserted-by":"publisher","first-page":"1240019","DOI":"10.1142\/S0219519412400192","volume":"12","author":"SD Puthankattil","year":"2012","unstructured":"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","journal-title":"J Mech Med Biol"},{"issue":"3","key":"17369_CR9","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1177\/1550059413480504","volume":"44","author":"M Ahmadlou","year":"2013","unstructured":"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\u2013181","journal-title":"EEG Clin Neurosci"},{"issue":"2013","key":"17369_CR10","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1016\/j.cmpb.2012.10.008","volume":"109","author":"B Hosseinifarda","year":"2013","unstructured":"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\u2013345","journal-title":"Comput Methods Programs Biomed"},{"issue":"3","key":"17369_CR11","doi-asserted-by":"publisher","first-page":"1450035","DOI":"10.1142\/S0219519414500353","volume":"14","author":"O Faust","year":"2014","unstructured":"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","journal-title":"J Mech Med Biol"},{"key":"17369_CR12","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1109\/CogInfoCom.2017.8268245"},{"key":"17369_CR13","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1109\/SLT.2016.7846256"},{"key":"17369_CR14","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1109\/ICETET.2013.32"},{"issue":"2","key":"17369_CR15","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1109\/T-AFFC.2012.38","volume":"4","author":"Y Yang","year":"2013","unstructured":"Yang Y, Fairbairn C, Cohn JF (2013) Detecting depression severity from vocal prosody. IEEE Trans Affect Comput 4(2):142\u2013150","journal-title":"IEEE Trans Affect Comput"},{"key":"17369_CR16","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.specom.2015.03.004","volume":"71","author":"N Cummins","year":"2015","unstructured":"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\u201349","journal-title":"Speech Commun"},{"issue":"3","key":"17369_CR17","doi-asserted-by":"publisher","first-page":"857","DOI":"10.2466\/pms.2001.92.3.857","volume":"92","author":"T Suslow","year":"2001","unstructured":"Suslow T, Junghanns K, Arolt V (2001) Detection of facial expressions of emotions in depression. Percept Mot Skills 92(3):857\u2013868","journal-title":"Percept Mot Skills"},{"key":"17369_CR18","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1109\/ACII.2009.5349358"},{"key":"17369_CR19","doi-asserted-by":"crossref","unstructured":"Meftah T, Thanh NL, Amar CB (2012) Detecting depression using multimodal approach of emotion recognition. In: International Conference on Complex Systems (ICCS), Agadir, Morocco","DOI":"10.1109\/ICoCS.2012.6458534"},{"issue":"6","key":"17369_CR20","doi-asserted-by":"publisher","first-page":"238","DOI":"10.1049\/htl.2016.0096","volume":"4","author":"A Sau","year":"2017","unstructured":"Sau A, Bhakta I (2017) Predicting anxiety and depression in elderly patients using machine learning technology. Healthc Technol Lett 4(6):238\u201343","journal-title":"Healthc Technol Lett"},{"key":"17369_CR21","first-page":"1","volume":"13","author":"D Choudhury","year":"2013","unstructured":"Choudhury D, Gamon M, Counts S, Horvitz E (2013) Predicting depression via social media. ICWSM 13:1\u201310","journal-title":"ICWSM"},{"key":"17369_CR22","doi-asserted-by":"publisher","first-page":"134804","DOI":"10.1016\/j.neulet.2020.134804","volume":"721","author":"KS Na","year":"2020","unstructured":"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)","journal-title":"Neurosci Lett"},{"key":"17369_CR23","doi-asserted-by":"publisher","first-page":"625","DOI":"10.3928\/0048-5713-19931101-09[CrossRef]","volume":"23","author":"BJ Mason","year":"1993","unstructured":"Mason BJ, Kocsis JH, Leon AC (1993) Measurement of severity and treatment response in dysthymia. Psychiatric Ann 23:625\u2013631. \nhttps:\/\/doi.org\/10.3928\/0048-5713-19931101-09","journal-title":"Psychiatric Ann"},{"key":"17369_CR24","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1136\/jnnp.23.1.56","volume":"23","author":"M Hamilton","year":"1960","unstructured":"Hamilton M (1960) A rating scale for depression. J Neurol Neurosurg Psychiatry 23:56\u201362","journal-title":"J Neurol Neurosurg Psychiatry"},{"key":"17369_CR25","volume-title":"Assessment scales in depression, mania and anxiety","author":"R Lam","year":"2006","unstructured":"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"},{"key":"17369_CR26","unstructured":"Clinical depression, DepressionFree.com. http:\/\/www.depressionfree.com\/clinical.html. Accessed 11 Mar 2022"},{"key":"17369_CR27","unstructured":"Non-clinical depression, DepressionFree.com. http:\/\/www.depressionfree.com\/non-clinical.html. Accessed 11 Mar 2022"},{"key":"17369_CR28","unstructured":"Classification - Machine Learning. https:\/\/www.simplilearn.com\/classification-machine-learning-tutorial. Accessed 21 Jan 2023"},{"key":"17369_CR29","unstructured":"Mohapatra S (2008) Development of impulse noise detection schemes for selective filtering, Master\u2019s thesis. National Institute of Technology Rourkela"},{"key":"17369_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.59543\/ijmscs.v1i.7693","volume":"1","author":"AO Salman","year":"2023","unstructured":"Salman AO, Geman O (2023) Evaluating three machine learning classification methods for effective COVID-19 diagnosis. Int J Math Stat Comput Sci 1:1\u201314. https:\/\/doi.org\/10.59543\/ijmscs.v1i.7693","journal-title":"Int J Math Stat Comput Sci"},{"key":"17369_CR31","unstructured":"Upadhyay DK, Mohapatra S, Singh NK (2023) Heterogeneous Bootstrapped Ensemble Model for an Early Assessment of MDD. J Harbin Eng Univ (ISSN: 1006\u20137043) 44(8)"},{"key":"17369_CR32","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1201\/9781003332312-15"},{"issue":"6","key":"17369_CR33","doi-asserted-by":"publisher","first-page":"1258","DOI":"10.1109\/TIM.2018.2799059","volume":"67","author":"E Alickovic","year":"2018","unstructured":"Alickovic E, Subasi A (2018) Ensemble SVM method for automatic sleep stage classification. IEEE Trans Instrum Meas 67(6):1258\u20131265","journal-title":"IEEE Trans Instrum Meas"},{"key":"17369_CR34","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1109\/BigComp.2018.00044","volume":"2018","author":"Z Chen","year":"2018","unstructured":"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\u2013256. https:\/\/doi.org\/10.1109\/BigComp.2018.00044","journal-title":"IEEE Int Conf Big Data Smart Comput (BigComp)"},{"key":"17369_CR35","doi-asserted-by":"publisher","DOI":"10.1080\/07357907.2017.1363892","author":"J Cvetkovi\u0107","year":"2017","unstructured":"Cvetkovi\u0107 J (2017) Breast cancer patients\u2019 depression prediction by machine learning approach. Cancer Invest. https:\/\/doi.org\/10.1080\/07357907.2017.1363892","journal-title":"Cancer Invest"},{"key":"17369_CR36","unstructured":"Krishni (2018) K-fold cross validation. Available at: https:\/\/medium.com\/datadriveninvestor\/k-fold-cross-validation-6b8518070833. Accessed 23 Jan 2023"},{"key":"17369_CR37","doi-asserted-by":"publisher","unstructured":"Yadav A, Vishwakarma D (2020) A Multilingual Framework of CNN and Bi-LSTM for Emotion Classification\u201d,. Available: https:\/\/doi.org\/10.1109\/ICCCNT49239.2020.9225614","DOI":"10.1109\/ICCCNT49239.2020.9225614"},{"issue":"1","key":"17369_CR38","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.cmpb.2005.02.007","volume":"79","author":"AM Brown","year":"2005","unstructured":"Brown AM (2005) A new software for carrying out one-way ANOVA post hoc tests. Comput Methods Programs Biomed 79(1):89\u201395. https:\/\/doi.org\/10.1016\/j.cmpb.2005.02.007. (ISSN 0169-2607)","journal-title":"Comput Methods Programs Biomed"},{"key":"17369_CR39","doi-asserted-by":"publisher","unstructured":"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\u2013794. https:\/\/doi.org\/10.1109\/TENSYMP46218.2019.8971369","DOI":"10.1109\/TENSYMP46218.2019.8971369"},{"key":"17369_CR40","doi-asserted-by":"publisher","first-page":"37033","DOI":"10.1007\/s11042-021-11488-6","volume":"81","author":"S Goyal","year":"2022","unstructured":"Goyal S, Bhatia PK (2022) Heterogeneous stacked ensemble classifier for software defect prediction. Multimed Tools Appl 81:37033\u201337055. https:\/\/doi.org\/10.1007\/s11042-021-11488-6","journal-title":"Multimed Tools Appl"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-17369-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-17369-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-17369-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,7]],"date-time":"2024-05-07T11:18:54Z","timestamp":1715080734000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-17369-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,27]]},"references-count":40,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["17369"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-17369-4","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,27]]},"assertion":[{"value":"18 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 August 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 October 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 October 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interests"}}]}}