{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T00:30:09Z","timestamp":1782952209094,"version":"3.54.5"},"reference-count":34,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2021,9,14]],"date-time":"2021-09-14T00:00:00Z","timestamp":1631577600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>We apply tree-based classification algorithms, namely the classification trees, with the use of the rpart algorithm, random forests and XGBoost methods to detect mood disorder in a group of 2508 lower secondary school students. The dataset presents many challenges, the most important of which is many missing data as well as the being heavily unbalanced (there are few severe mood disorder cases). We find that all algorithms are specific, but only the rpart algorithm is sensitive; i.e., it is able to detect cases of real cases mood disorder. The conclusion of this paper is that this is caused by the fact that the rpart algorithm uses the surrogate variables to handle missing data. The most important social-studies-related result is that the adolescents\u2019 relationships with their parents are the single most important factor in developing mood disorders\u2014far more important than other factors, such as the socio-economic status or school success.<\/jats:p>","DOI":"10.3390\/e23091210","type":"journal-article","created":{"date-parts":[[2021,9,14]],"date-time":"2021-09-14T03:46:14Z","timestamp":1631591174000},"page":"1210","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Mood Disorder Detection in Adolescents by Classification Trees, Random Forests and XGBoost in Presence of Missing Data"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8236-7206","authenticated-orcid":false,"given":"Elzbieta","family":"Turska","sequence":"first","affiliation":[{"name":"Institute of Pedagogy, University of Zielona Gora, 65-417 Zielona Gora, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1304-5393","authenticated-orcid":false,"given":"Szymon","family":"Jurga","sequence":"additional","affiliation":[{"name":"Department of Neurology, Collegium Medicum, University Hospital, University of Zielona G\u00f3ra, 65-417 Zielona Gora, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0866-734X","authenticated-orcid":false,"given":"Jaroslaw","family":"Piskorski","sequence":"additional","affiliation":[{"name":"Institute of Physics, University of Zielona Gora, 65-417 Zielona Gora, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Liberska, H., and Farnicka, M. (2016). Family Dysfunctionality as a Risk for Mood Disorders in Adolescents. Aggression as a Challenge. Theory and Research. Current Problems, Peter Lang Edition.","DOI":"10.3726\/978-3-653-05056-1\/22"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1037\/0003-066X.48.2.155","article-title":"Depression in adolescence","volume":"48","author":"Petersen","year":"1993","journal-title":"Am. Psychol."},{"key":"ref_3","unstructured":"Carr, A. (2008). Depression and Attempted Suicide in Adolescents, Wiley-Blackwell."},{"key":"ref_4","unstructured":"Carson, R., Mineka, S., and Butcher, J. (2000). Abnormal Psychology and Modern Life, Allyn and Bacon."},{"key":"ref_5","unstructured":"Fox, C., and Hawthon, K. (2004). Deliberate Self-Harm in Adolescence, Jessica Kingsley."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1007\/s12552-011-9047-1","article-title":"Low social status markers: Do they predict depressive symptoms in adolescence?","volume":"3","author":"Jackson","year":"2011","journal-title":"Race Soc. Probl."},{"key":"ref_7","unstructured":"Burns, D. (2008). Feeling Good. The New Mood Therapy, Harper Collins."},{"key":"ref_8","unstructured":"Mi\u0142kowska, G. (2012). \u017bycie Lubuszan: Wsp\u00f3\u0142czesno\u015b\u0107 i Perspektywy Lubuskiej M\u0142odzie.zy, PRINTPAP."},{"key":"ref_9","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning: Data MINING, Inference, and Prediction, Springer Science & Business Media. [2nd ed.]."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An Introduction to Statistical Learning, Springer.","DOI":"10.1007\/978-1-4614-7138-7"},{"key":"ref_11","unstructured":"Rhys, H.I. (2020). Machine Learning with R, the Tidyverse and Mlr, Manning Publications Co."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10115-007-0114-2","article-title":"Top 10 algorithms in data mining","volume":"14","author":"Wu","year":"2008","journal-title":"Knowl. Inf. Syst."},{"key":"ref_13","unstructured":"Therneau, T.M., and Atkinson, E.J. (2021, June 28). Mayo Foundation. An Introduction to Recursive Partitioning Using the RPART Routines. Available online: https:\/\/cran.r-project.org\/web\/packages\/rpart\/vignettes\/longintro.pdf."},{"key":"ref_14","unstructured":"Mansour, Y. (1997, January 8\u201312). Pessimistic decision tree pruning based on tree size. Proceedings of the 14th International Conference on Machine Learning, Nashville, TN, USA."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1017\/S0269888997000015","article-title":"Simplifying Decision Trees: A Survey","volume":"12","author":"Breslow","year":"1997","journal-title":"Knowl. Eng. Rev."},{"key":"ref_16","unstructured":"Breiman, L. (2021, June 30). Manual for Setting Up, Using and Understanding Random Forest V40. Available online: https:\/\/www.stat.berkeley.edu\/~breiman\/Using_random_forests_v4.0.pdf."},{"key":"ref_17","unstructured":"Kearns, M. (1988). Thoughts on Hypothesis Boosting. Machine Learning Class Project, Unpublished manuscript."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kearns, M., and Valiant, L. (1989). Cryptographic [sic] Limitations on Learning Boolean Formulae and Finite Automata, Symposium on Theory of Computing.","DOI":"10.1145\/73007.73049"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy Function Approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_21","unstructured":"Fay, R.E. Valid inferences from imputed survey data. Proceedings of the Section on Survey Research Methods."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Agresti, A. (2007). An Introduction to Categorical Data Analysis, Wiley-Interscience.","DOI":"10.1002\/0470114754"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1002\/sam.11348","article-title":"Random forest missing data algorithms","volume":"10","author":"Tang","year":"2017","journal-title":"Stat. Anal. Data Min. ASA Data Sci. J."},{"key":"ref_24","unstructured":"Enders, C.K. (2010). Applied Missing Data Analysis, Guilford Press."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Gelman, A., Carlin, J.B., Stern, H.S., and Rubin, D.B. (2014). Bayesian Data Analysis, Taylor Francis.","DOI":"10.1201\/b16018"},{"key":"ref_26","unstructured":"Therneau, T., and Atkinson, B. (2021, June 28). rpart: Recursive Partitioning and Regression Trees, R Package Version 4.1-15. Available online: https:\/\/CRAN.R-project.org\/package=rpart."},{"key":"ref_27","first-page":"18","article-title":"Classification and Regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_28","unstructured":"Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., and Zhou, T. (2021, June 28). XGBoost: Extreme Gradient Boosting. R Package Version 1.3.2.1. Available online: https:\/\/CRAN.R-project.org\/package=xgboost."},{"key":"ref_29","first-page":"1","article-title":"mlr: Machine Learning in R","volume":"17","author":"Bischl","year":"2016","journal-title":"J. Mach. Learn. Res."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"726","DOI":"10.1177\/1403494820902914","article-title":"Poor family relationships in adolescence as a risk factor of in-patient psychiatric care across the life course: A prospective cohort study","volume":"48","author":"Alm","year":"2020","journal-title":"Scand. J. Public Health"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.psychres.2005.08.015","article-title":"ESEMeD\/MHEDEA 2000 investigators Relationship between parental bonding and mood disorder in six European countries","volume":"30","author":"Heider","year":"2006","journal-title":"Psychiatry Res."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Sawangarreerak, S., and Thanathamathee, P. (2020). Random Forest with Sampling Techniques for Handling Imbalanced Prediction of University Student Depression. Information, 11.","DOI":"10.3390\/info11110519"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Hooshyar, D., Pedaste, M., and Yang, Y. (2020). Mining educational data to predict students\u2019 performance through procrastination behavior. Entropy, 22.","DOI":"10.3390\/e22010012"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Foster, I., Ghani, R., Jarmin, R.S., Kreuter, F., and Lane, J. (2017). Big Data and Social Science: A Practical Guide to Methods and Tools, Chapman & Hall\/CRC Press.","DOI":"10.1201\/9781315368238"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/23\/9\/1210\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:02:15Z","timestamp":1760166135000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/23\/9\/1210"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,14]]},"references-count":34,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["e23091210"],"URL":"https:\/\/doi.org\/10.3390\/e23091210","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,14]]}}}