{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T22:10:57Z","timestamp":1785449457018,"version":"3.56.0"},"reference-count":40,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T00:00:00Z","timestamp":1758240000000},"content-version":"vor","delay-in-days":261,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000804","name":"Eurostat","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000804","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Procedia Computer Science"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1016\/j.procs.2025.09.190","type":"journal-article","created":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T22:13:48Z","timestamp":1762467228000},"page":"703-712","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["Household financial distress in the Visegrad Group countries: comparative analysis using machine learning methods"],"prefix":"10.1016","volume":"270","author":[{"given":"Urszula","family":"Grzybowska","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Agnieszka","family":"Wojew\u00f3dzka-Wiewi\u00f3rska","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Piotr","family":"Stachura","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanna","family":"Dudek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"4","key":"10.1016\/j.procs.2025.09.190_bib1","doi-asserted-by":"crossref","first-page":"1305","DOI":"10.24136\/eq.3049","article-title":"\u201cInability to face unexpected expenses and monetary poverty in Poland: Are these two faces on the same coin?.\u201d","volume":"19","author":"Dudek","year":"2024","journal-title":"Equilibrium. Quarterly Journal of Economics and Economic Policy"},{"key":"10.1016\/j.procs.2025.09.190_bib2","doi-asserted-by":"crossref","first-page":"999","DOI":"10.1007\/s11205-022-03058-6","article-title":"\u201cComparing material and social deprivation indicators: identification of deprived populations.\u201d","volume":"165","author":"Fabrizi","year":"2023","journal-title":"Social Indicators Research"},{"issue":"3","key":"10.1016\/j.procs.2025.09.190_bib3","doi-asserted-by":"crossref","first-page":"995","DOI":"10.1007\/s40888-021-00228-6","article-title":"\u201cSocial exclusion and financial distress: evidence from Italy and Spain.\u201d","volume":"38","author":"Mussida","year":"2021","journal-title":"Economia Politica"},{"key":"10.1016\/j.procs.2025.09.190_bib4","doi-asserted-by":"crossref","unstructured":"Lusardi, Annamaria, Daniel J. Schneider, and Peter Tufano (2011) \u201cFinancially Fragile Households: Evidence and Implications.\u201d NBER Working Papers 17072, National Bureau of Economic Research, Inc.","DOI":"10.3386\/w17072"},{"issue":"4","key":"10.1016\/j.procs.2025.09.190_bib5","doi-asserted-by":"crossref","first-page":"47","DOI":"10.18778\/1508-2008.27.30","article-title":"\u201cThe Labour Market Situation of Population Groups in the Visegr\u00e1d Countries.\u201d","volume":"27","author":"Arendt","year":"2024","journal-title":"Comparative Economic Research. Central and Eastern Europe"},{"key":"10.1016\/j.procs.2025.09.190_bib6","doi-asserted-by":"crossref","unstructured":"Bieszk-Stolorz, Beata, and Krzysztof Dmytr\u00f3w (2020) \u201cInfluence of accession of the Visegrad Group Countries to the EU on the situation in their labour markets.\u201d Sustainability 12(16), 6694. https:\/\/doi.org\/10.3390\/su12166694","DOI":"10.3390\/su12166694"},{"issue":"2","key":"10.1016\/j.procs.2025.09.190_bib7","first-page":"132","article-title":"\u201cThe risk and severity of food insecurity in V4 Countries: Insight from the fuzzy approach. \u201d","volume":"33","author":"Dudek","year":"2022","journal-title":"Inzinerine Ekonomika-Engineering Economics"},{"key":"10.1016\/j.procs.2025.09.190_bib8","doi-asserted-by":"crossref","first-page":"4441","DOI":"10.1016\/j.procs.2024.09.294","article-title":"\u201cSocioeconomic factors associated with household overcrowding in the Visegrad Group countries\u2013analysis based on machine learning approach\u201d","volume":"246","author":"Grzybowska","year":"2024","journal-title":"Procedia Computer Science"},{"key":"10.1016\/j.procs.2025.09.190_bib9","unstructured":"Chrzanowska, Mariola, Joanna Landmesser, and Monika Zieli\u0144ska-Sitkiewicz (2018) \u201cQuality of life in the Visegrad group countries\u2013a multidimensional approach\u201d. In M. Reiff, P. Ge\u017eik (eds.) Quantitative Methods in Economics. Multiple Criteria Decision Making XIX: Proceedings of the International Scientific Conference: 41\u201349. Bratislava, Letra Edu."},{"issue":"1","key":"10.1016\/j.procs.2025.09.190_bib10","doi-asserted-by":"crossref","first-page":"282","DOI":"10.14254\/2071-789X.2016\/9-1\/19","article-title":"\u201cQuality of Life Research: Material Living Conditions in the Visegrad Group Countries.\u201d","volume":"9","author":"Nov\u00e1kov\u00e1","year":"2016","journal-title":"Economics and Sociology"},{"key":"10.1016\/j.procs.2025.09.190_bib11","unstructured":"Eurostat (2025a) \u201cInability to face unexpected financial expenses.\u201d Eurostat database. Accessed from https:\/\/ec.europa.eu\/eurostat\/databrowser\/view\/ILC_MDES04__custom_12132757\/default\/table?lang=en, accessed 20 February 2025."},{"key":"10.1016\/j.procs.2025.09.190_bib12","doi-asserted-by":"crossref","first-page":"3113","DOI":"10.1016\/j.procs.2023.10.305","article-title":"\u201cAnalysis of material deprivation in Poland: a machine learning approach\u201d","volume":"225","author":"Grzybowska","year":"2023","journal-title":"Procedia Computer Science"},{"issue":"24","key":"10.1016\/j.procs.2025.09.190_bib13","doi-asserted-by":"crossref","first-page":"6310","DOI":"10.3390\/en17246310","article-title":"\u201cHouseholds Vulnerable to Energy Poverty in the Visegrad Group Countries: An Analysis of Socio-Economic Factors Using a Machine Learning Approach.\u201d","volume":"17","author":"Grzybowska","year":"2024","journal-title":"Energies"},{"issue":"10","key":"10.1016\/j.procs.2025.09.190_bib14","doi-asserted-by":"crossref","first-page":"3851","DOI":"10.1093\/rfs\/hhz009","article-title":"\u201cThe Persistence of Financial Distress.\u201d","volume":"32","author":"Athreya","year":"2019","journal-title":"The Review of Financial Studies"},{"issue":"9","key":"10.1016\/j.procs.2025.09.190_bib15","doi-asserted-by":"crossref","first-page":"3425","DOI":"10.1016\/j.jbankfin.2013.05.005","article-title":"\u201cPersistency of financial distress amongst Italian households: Evidence from dynamic models for binary panel data.\u201d","volume":"37","author":"Giarda","year":"2013","journal-title":"Journal of Banking & Finance"},{"key":"10.1016\/j.procs.2025.09.190_bib16","doi-asserted-by":"crossref","first-page":"216","DOI":"10.14254\/2071-8330.2020\/13-3\/14","article-title":"\u201cLogistic regression in the analysis of unexpected household expenses: Cross-country evidence\u201d","volume":"13","author":"Kowalczyk-R\u00f3lczy\u0144ska","year":"2020","journal-title":"Journal of International Studies"},{"key":"10.1016\/j.procs.2025.09.190_bib17","doi-asserted-by":"crossref","unstructured":"Fabrizi, Enrico, Chiara Mussida, and Maria Laura Parisi (2025) \u201cMaterial and social deprivation among one-person households: the role of gender.\u201d Journal of Population Economics, 38, 10 https:\/\/doi.org\/10.1007\/s00148-025-01084-5.","DOI":"10.1007\/s00148-025-01084-5"},{"key":"10.1016\/j.procs.2025.09.190_bib18","doi-asserted-by":"crossref","unstructured":"Mussida, Chiara, Maria Laura Parisi, and Nicola Pontarollo (2023) \u201cSeverity of material deprivation in Spanish regions and the role of the European Structural Funds.\u201d Socio-economic Planning Sciences 88:101651.","DOI":"10.1016\/j.seps.2023.101651"},{"issue":"1","key":"10.1016\/j.procs.2025.09.190_bib19","doi-asserted-by":"crossref","first-page":"23","DOI":"10.14254\/2071-789X.2024\/17-1\/2","article-title":"\u201cMaterial and social deprivation in the European Union: Country-level analysis.\u201d","volume":"17","author":"Sedefo\u011flu","year":"2024","journal-title":"Economics and Sociology"},{"issue":"1","key":"10.1016\/j.procs.2025.09.190_bib20","first-page":"100","article-title":"\u201cMaterial Deprivation as Exemplified by Selected Countries of the European Union.\u201d","volume":"33","author":"Wyduba","year":"2017","journal-title":"Intercatedra"},{"key":"10.1016\/j.procs.2025.09.190_bib21","doi-asserted-by":"crossref","first-page":"3122","DOI":"10.1016\/j.procs.2024.09.360","article-title":"\u201cOne-dimensional and multidimensional affluence: analysis of determinants using a supervised learning algorithm.\u201d","volume":"246","author":"S\u0105czewska-Piotrowska","year":"2024","journal-title":"Procedia Computer Science"},{"key":"10.1016\/j.procs.2025.09.190_bib22","doi-asserted-by":"crossref","first-page":"1025","DOI":"10.1007\/s11205-015-1212-2","article-title":"\u201cThe interrelationships between the Europe 2020 Poverty and Social Exclusion Indicators.\u201d","volume":"130","author":"Ayll\u00f3n","year":"2017","journal-title":"Social Indicators Research"},{"issue":"4","key":"10.1016\/j.procs.2025.09.190_bib23","doi-asserted-by":"crossref","first-page":"69","DOI":"10.22630\/ASPE.2020.19.4.42","article-title":"\u201cPoverty in rural areas: an outline of the problem.\u201d","volume":"19","author":"Kalinowski","year":"2020","journal-title":"Acta Scientiarum Polonorum. Oeconomia"},{"issue":"11","key":"10.1016\/j.procs.2025.09.190_bib24","doi-asserted-by":"crossref","first-page":"160","DOI":"10.3390\/data7110160","article-title":"\u201cExplainable Machine Learning for Financial Distress Prediction: Evidence from Vietnam\u201d","volume":"7","author":"Tran","year":"2022","journal-title":"Data"},{"key":"10.1016\/j.procs.2025.09.190_bib25","doi-asserted-by":"crossref","unstructured":"Chen, Xiaofang, Zengli Mao, and Chong Wu (2024) \u201cMulti-class Financial Distress Prediction Based on Feature Selection and Deep Forest Algorithm.\u201d Computational Economics. Doi: 10.1007\/s10614-024-10761-8","DOI":"10.1007\/s10614-024-10761-8"},{"key":"10.1016\/j.procs.2025.09.190_bib26","doi-asserted-by":"crossref","unstructured":"Martono, Niken Prasasti, and Hayato Ohwada (2023) Financial Distress Model Prediction Using Machine Learning: A Case Study on Indonesia\u2019s Consumers Cyclical Companies. In: Koprinska, I., et al. Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2022. Communications in Computer and Information Science, vol 1753. Springer, Cham. Doi: 10.1007\/978-3-031-23633-4_5.","DOI":"10.1007\/978-3-031-23633-4_5"},{"key":"10.1016\/j.procs.2025.09.190_bib27","doi-asserted-by":"crossref","unstructured":"de Waal, Hendrik, Serge Nyawa, and Samuel Fosso Wamba (2024) \u201cConsumers\u2019 Financial Distress: Prediction and Prescription Using Interpretable Machine Learning.\u201d Information Systems Frontiers. Doi: 10.1007\/s10796-024-10501-1","DOI":"10.1007\/s10796-024-10501-1"},{"key":"10.1016\/j.procs.2025.09.190_bib28","doi-asserted-by":"crossref","unstructured":"Kim, Ji Yoon (2021) \u201cUsing Machine Learning to Predict Poverty Status in Costa Rican Households\u201d. Available at SSRN: https:\/\/ssrn.com\/abstract=3971979, Doi: 10.2139\/ssrn.3971979 (arXiv:2111.13319).","DOI":"10.2139\/ssrn.3971979"},{"key":"10.1016\/j.procs.2025.09.190_bib29","unstructured":"Ke, Guolin, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu (2017) \u201cLightGBM: A Highly Efficient Gradient Boosting Decision Tree.\u201d Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, 4-9 December 2017, 314s9-3157. https:\/\/dl.acm.org\/doi\/10.5555\/3294996.3295074"},{"issue":"1","key":"10.1016\/j.procs.2025.09.190_bib30","doi-asserted-by":"crossref","first-page":"2465971","DOI":"10.1080\/23322039.2025.2465971","article-title":"\u201cComparative analysis of boosting algorithms for predicting personal default.\u201d","volume":"13","author":"Nguyen","year":"2025","journal-title":"Cogent Economics & Finance"},{"key":"10.1016\/j.procs.2025.09.190_bib31","unstructured":"Salvador, Erika L. (2024) \u201cUse of Boosting Algorithms in Household-Level Poverty Measurement: A Machine Learning Approach to Predict and Classify Household Wealth Quintiles in the Philippines\u201d preprint arXiv:2407.13061. Doi: 10.48550\/arXiv.2407.13061"},{"key":"10.1016\/j.procs.2025.09.190_bib32","unstructured":"Eurostat (2022) \u201cEU statistics on income and living conditions (EU-SILC) methodology\u2013sampling.\u201d Statistics explained. Accessed from https:\/\/ec.europa.eu\/eurostat\/statistics-explained\/SEPDF\/cache\/39791.pdf."},{"key":"10.1016\/j.procs.2025.09.190_bib33","unstructured":"Eurostat (2025b) \u201cMean and median income by age and sex\u201d, \u201cGDP per capita in PPS\u201d Eurostat database. Accessed from https:\/\/ec.europa.eu\/eurostat\/databrowser\/view\/ilc_di03\/default\/table?lang=en, accessed 23 March 2025."},{"key":"10.1016\/j.procs.2025.09.190_bib34","unstructured":"UNDP (2025), http:\/\/hdr.undp.org, accessed 3 March 2025."},{"key":"10.1016\/j.procs.2025.09.190_bib35","unstructured":"Chen, Chao, Andy Liaw, and Leo Breiman (2004) Using random forest to learn imbalanced data. Technical Report, University of California, Berkeley 110(1\u201312): 24. Accessed from https:\/\/statistics.berkeley.edu\/sites\/default\/files\/tech-reports\/666.pdf."},{"key":"10.1016\/j.procs.2025.09.190_bib36","doi-asserted-by":"crossref","unstructured":"Chen, Tianqi, and Carlos Guestrin (2016) \u201cXGBoost: A Scalable Tree Boosting System\u201d. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining: 785\u2013794. New York, NY, USA, ACM. https:\/\/doi.org\/10.1145\/2939672.2939785","DOI":"10.1145\/2939672.2939785"},{"key":"10.1016\/j.procs.2025.09.190_bib37","doi-asserted-by":"crossref","unstructured":"Breiman, Leo (2001) \u201cRandom Forests\u201c. Machine Learning 45(1): 5\u201332.","DOI":"10.1023\/A:1010933404324"},{"key":"10.1016\/j.procs.2025.09.190_bib38","unstructured":"Dorogush, Anna Veronika, Vasily Ershov, and Andrey Gulin (2017) CatBoost: gradient boosting with categorical features support. Workshop on ML Systems at NIPS 2017. Accessed from https:\/\/catboost.ai\/en\/docs\/concepts\/educational-materials-papers#catboost-unbiased-boosting-with-categorical-features."},{"key":"10.1016\/j.procs.2025.09.190_bib39","unstructured":"Prokhorenkova, Liudmila, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, and Andrey Gulin (2018) \u201cCatBoost: unbiased boosting with categorical features\u201d. In Proceedings of the 32nd International Conference on Neural Information Processing Systems NeurIPS: 6639\u20136649."},{"key":"10.1016\/j.procs.2025.09.190_bib40","unstructured":"Microsoft (2017) https:\/\/lightgbm.readthedocs.io\/en\/stable\/ accessed 23 March 2025."}],"container-title":["Procedia Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1877050925028601?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1877050925028601?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,12,21]],"date-time":"2025-12-21T08:28:09Z","timestamp":1766305689000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1877050925028601"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":40,"alternative-id":["S1877050925028601"],"URL":"https:\/\/doi.org\/10.1016\/j.procs.2025.09.190","relation":{},"ISSN":["1877-0509"],"issn-type":[{"value":"1877-0509","type":"print"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Household financial distress in the Visegrad Group countries: comparative analysis using machine learning methods","name":"articletitle","label":"Article Title"},{"value":"Procedia Computer Science","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.procs.2025.09.190","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Author(s). Published by Elsevier B.V.","name":"copyright","label":"Copyright"}]}}