{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T22:50:24Z","timestamp":1793227824263,"version":"4.5.0"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T00:00:00Z","timestamp":1765756800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T00:00:00Z","timestamp":1765756800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Gastroenterol"],"DOI":"10.1186\/s12876-025-04461-2","type":"journal-article","created":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T14:47:22Z","timestamp":1765810042000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Development of a prediction model for peptic ulcer disease using the fusion of a feature selection strategy and ensemble algorithms"],"prefix":"10.1186","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3770-2375","authenticated-orcid":false,"given":"Raoof","family":"Nopour","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,15]]},"reference":[{"issue":"2","key":"4461_CR1","first-page":"1","volume":"1","author":"G Bereda","year":"2022","unstructured":"Bereda G. Peptic ulcer disease: definition, pathophysiology, and treatment. J Biomed Biol Sci. 2022;1(2):1\u201310.","journal-title":"J Biomed Biol Sci"},{"issue":"2","key":"4461_CR2","doi-asserted-by":"publisher","DOI":"10.3390\/pathogens9020104","volume":"9","author":"R D\u00e1vila-Collado","year":"2020","unstructured":"D\u00e1vila-Collado R, Jarqu\u00edn-Dur\u00e1n O, Dong LT, Espinoza JL. Epstein\u2013barr virus and Helicobacter pylori co-infection in non-malignant gastroduodenal disorders. Pathogens. 2020;9(2):104.","journal-title":"Pathogens"},{"issue":"8","key":"4461_CR3","doi-asserted-by":"publisher","first-page":"662","DOI":"10.2174\/1389557520666191227151939","volume":"20","author":"H Ardalani","year":"2020","unstructured":"Ardalani H, Hadipanah A, Sahebkar A. Medicinal plants in the treatment of peptic ulcer disease: A review. Mini Rev Med Chem. 2020;20(8):662\u2013702.","journal-title":"Mini Rev Med Chem"},{"issue":"6","key":"4461_CR4","first-page":"97","volume":"10","author":"SB Zibima","year":"2020","unstructured":"Zibima SB, Oniso JI, Wasini KB, Ogu JC. Prevalence trends and associated modifiable risk factors of peptic ulcer disease among students in a university community south-south Nigeria. Int J Health Sci Res. 2020;10(6):97\u2013105.","journal-title":"Int J Health Sci Res"},{"issue":"5","key":"4461_CR5","doi-asserted-by":"publisher","first-page":"913","DOI":"10.1007\/s12262-021-03189-z","volume":"84","author":"N Salari","year":"2022","unstructured":"Salari N, Darvishi N, Shohaimi S, Bartina Y, Ahmadipanah M, Salari HR, et al. The global prevalence of peptic ulcer in the world: a systematic review and meta-analysis. Indian J Surg. 2022;84(5):913\u201321.","journal-title":"Indian J Surg"},{"issue":"1","key":"4461_CR6","doi-asserted-by":"publisher","DOI":"10.1136\/bmjgast-2019-000350","volume":"7","author":"S Peiffer","year":"2020","unstructured":"Peiffer S, Pelton M, Keeney L, Kwon EG, Ofosu-Okromah R, Acharya Y, et al. Risk factors of perioperative mortality from complicated peptic ulcer disease in Africa: systematic review and meta-analysis. BMJ Open Gastroenterol. 2020;7(1):e000350.","journal-title":"BMJ Open Gastroenterol"},{"issue":"6","key":"4461_CR7","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1016\/j.medcle.2023.05.015","volume":"161","author":"I Laucirica","year":"2023","unstructured":"Laucirica I, Garcia Iglesias P, Calvet X. Peptic ulcer. Medicina Cl\u00ednica (English Edition). 2023;161(6):260\u20136.","journal-title":"Medicina Cl\u00ednica (English Edition)"},{"key":"4461_CR8","doi-asserted-by":"publisher","DOI":"10.1177\/17562848231210375","volume":"16","author":"Z Zhang","year":"2023","unstructured":"Zhang Z, Yan W, Zhang X, Wang J, Zhang Z, Lin Z, et al. Peptic ulcer disease burden, trends, and inequalities in 204 countries and territories, 1990\u20132019: a population-based study. Ther Adv Gastroenterol. 2023;16:17562848231210375.","journal-title":"Ther Adv Gastroenterol"},{"issue":"7","key":"4461_CR9","doi-asserted-by":"publisher","first-page":"856","DOI":"10.1177\/0003134820939929","volume":"86","author":"OA Olufajo","year":"2020","unstructured":"Olufajo OA, Wilson A, Yehayes B, Zeineddin A, Cornwell EE, Williams M. Trends in the surgical management and outcomes of complicated peptic ulcer disease. The American Surgeon\u2122. 2020;86(7):856\u201364.","journal-title":"The American Surgeon\u2122"},{"issue":"1","key":"4461_CR10","doi-asserted-by":"publisher","DOI":"10.1186\/s12876-022-02130-2","volume":"22","author":"X Xie","year":"2022","unstructured":"Xie X, Ren K, Zhou Z, Dang C, Zhang H. The global, regional and national burden of peptic ulcer disease from 1990 to 2019: a population-based study. BMC Gastroenterol. 2022;22(1):58.","journal-title":"BMC Gastroenterol"},{"key":"4461_CR11","doi-asserted-by":"crossref","unstructured":"Azhari H, King JA, Coward S, Windsor JW, Ma C, Shah SC et al. The global incidence of peptic ulcer disease is decreasing since the turn of the 21st century: A study of the organisation for economic Co-Operation and development (OECD). Official J Am Coll Gastroenterol | ACG. 2022;117(9):1419\u201327.","DOI":"10.14309\/ajg.0000000000001843"},{"issue":"35","key":"4461_CR12","doi-asserted-by":"publisher","first-page":"5302","DOI":"10.3748\/wjg.v26.i35.5302","volume":"26","author":"A Dadfar","year":"2020","unstructured":"Dadfar A, Edna TH. Epidemiology of perforating peptic ulcer: a population-based retrospective study over 40 years. World J Gastroenterol. 2020;26(35):5302\u201313.","journal-title":"World J Gastroenterol"},{"issue":"1","key":"4461_CR13","doi-asserted-by":"publisher","DOI":"10.1186\/s13017-019-0283-9","volume":"15","author":"A Tarasconi","year":"2020","unstructured":"Tarasconi A, Coccolini F, Biffl WL, Tomasoni M, Ansaloni L, Picetti E, et al. Perforated and bleeding peptic ulcer: WSES guidelines. World J Emerg Surg. 2020;15(1):3.","journal-title":"World J Emerg Surg"},{"issue":"4","key":"4461_CR14","first-page":"236","volume":"91","author":"J Fashner","year":"2015","unstructured":"Fashner J, Gitu AC. Diagnosis and treatment of peptic ulcer disease and H. pylori infection. Am Fam Physician. 2015;91(4):236\u201342.","journal-title":"Am Fam Physician"},{"key":"4461_CR15","unstructured":"Vijay N. Diagnostic Role of Endoscopy in Upper Gastrointestinal Diseases. Master's Thesis. Rajiv Gandhi University of Health Sciences (India). 2018."},{"issue":"1","key":"4461_CR16","doi-asserted-by":"publisher","DOI":"10.1080\/23311916.2020.1861730","volume":"8","author":"S Arab","year":"2021","unstructured":"Arab S, Rezaee K, Moghaddam G. A novel fuzzy expert system design to assist with peptic ulcer disease diagnosis. Cogent Engineering. 2021;8(1):1861730.","journal-title":"Cogent Engineering"},{"issue":"6","key":"4461_CR17","doi-asserted-by":"publisher","first-page":"07","DOI":"10.20510\/ukjpb\/5\/i6\/166561","volume":"5","author":"J Eniojukan","year":"2017","unstructured":"Eniojukan J, Okonkwo O, Adje U. Risk factors, management and other correlates of peptic ulcer disease in a university community in south-south Nigeria. Pharm Biosci J. 2017;5(6):07\u201315.","journal-title":"Pharm Biosci J"},{"issue":"5","key":"4461_CR18","doi-asserted-by":"publisher","first-page":"257","DOI":"10.36349\/easjnfs.2020.v02i05.003","volume":"2","author":"K Sadiq","year":"2020","unstructured":"Sadiq K, Rizwan B, Noreen S, Fatima A, Sheraz M, Shafqat M, et al. Determinants of peptic ulcer disease: a systematic review. EAS Journal of Nutrition and Food Sciences. 2020;2(5):257\u201364.","journal-title":"EAS Journal of Nutrition and Food Sciences"},{"issue":"1","key":"4461_CR19","doi-asserted-by":"publisher","DOI":"10.1155\/2023\/9418666","volume":"2023","author":"S Dalal","year":"2023","unstructured":"Dalal S, Goel P, Onyema EM, Alharbi A, Mahmoud A, Algarni MA, et al. Application of machine learning for cardiovascular disease risk prediction. Comput Intell Neurosci. 2023;2023(1):9418666.","journal-title":"Comput Intell Neurosci"},{"key":"4461_CR20","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2024.1343627","volume":"14","author":"S Hussain","year":"2024","unstructured":"Hussain S, Ali M, Naseem U, Nezhadmoghadam F, Jatoi MA, Gulliver TA, et al. Breast cancer risk prediction using machine learning: a systematic review. Front Oncol. 2024;14:1343627.","journal-title":"Front Oncol"},{"key":"4461_CR21","unstructured":"Ratner B. Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Third Edition (3rd ed.). Chapman and Hall\/CRC; New York. 2017;690."},{"key":"4461_CR22","doi-asserted-by":"crossref","unstructured":"Rajula HSR, Verlato G, Manchia M, Antonucci N, Fanos VJM. Comparison of conventional statistical methods with machine learning in medicine: diagnosis, drug development, and treatment. medicina. 2020;56(9):455.","DOI":"10.3390\/medicina56090455"},{"key":"4461_CR23","doi-asserted-by":"crossref","unstructured":"Iniesta R, Stahl D, McGuffin PJP. Machine learning, statistical learning and the future of biological research in psychiatry. Psychological Medicine. 2016;46(12):2455\u201365.","DOI":"10.1017\/S0033291716001367"},{"key":"4461_CR24","doi-asserted-by":"crossref","unstructured":"Moshawrab M, Adda M, Bouzouane A, Ibrahim H, Raad A. Reviewing federated machine learning and its use in diseases prediction. Sens. 2023;23(4):1\u201339.","DOI":"10.3390\/s23042112"},{"issue":"1","key":"4461_CR25","doi-asserted-by":"publisher","DOI":"10.1186\/s13063-021-05489-x","volume":"22","author":"EH Weissler","year":"2021","unstructured":"Weissler EH, Naumann T, Andersson T, Ranganath R, Elemento O, Luo Y, et al. The role of machine learning in clinical research: transforming the future of evidence generation. Trials. 2021;22(1):537.","journal-title":"Trials"},{"key":"4461_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105458","volume":"145","author":"M Shehab","year":"2022","unstructured":"Shehab M, Abualigah L, Shambour Q, Abu-Hashem MA, Shambour MKY, Alsalibi AI, et al. Machine learning in medical applications: a review of state-of-the-art methods. Comput Biol Med. 2022;145:105458.","journal-title":"Comput Biol Med"},{"issue":"2","key":"4461_CR27","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1007\/s11831-023-09996-9","volume":"31","author":"DD Solomon","year":"2024","unstructured":"Solomon DD, Sonia, Kumar K, Kanwar K, Iyer S, Kumar M. Extensive review on the role of machine learning for multifactorial genetic disorders prediction. Arch Comput Methods Eng. 2024;31(2):623\u201340.","journal-title":"Arch Comput Methods Eng"},{"key":"4461_CR28","doi-asserted-by":"crossref","unstructured":"Asif S, Wenhui Y, ur-Rehman S-, ul-ain Q-, Amjad K, Yueyang Y et al. Advancements and prospects of machine learning in medical diagnostics: unveiling the future of diagnostic precision. Arch Comput Methods Eng. 2024;32:1\u201331.","DOI":"10.1007\/s11831-024-10148-w"},{"issue":"6","key":"4461_CR29","doi-asserted-by":"publisher","DOI":"10.3390\/a13060140","volume":"13","author":"P Pintelas","year":"2020","unstructured":"Pintelas P, Livieris IE. Special issue on ensemble learning and applications. Algorithms. 2020;13(6):140.","journal-title":"Algorithms"},{"issue":"2","key":"4461_CR30","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1007\/s11704-019-8208-z","volume":"14","author":"X Dong","year":"2020","unstructured":"Dong X, Yu Z, Cao W, Shi Y, Ma Q. A survey on ensemble learning. Front Comput Sci. 2020;14(2):241\u201358.","journal-title":"Front Comput Sci"},{"issue":"4","key":"4461_CR31","first-page":"289","volume":"6","author":"BJ Lee","year":"2020","unstructured":"Lee BJ. Prediction model of peptic ulcer diseases in middle-aged and elderly adults based on machine learning. J Convergence Cult Technol. 2020;6(4):289\u201394.","journal-title":"J Convergence Cult Technol"},{"issue":"1","key":"4461_CR32","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.compeleceng.2013.11.024","volume":"40","author":"G Chandrashekar","year":"2014","unstructured":"Chandrashekar G, Sahin F. A survey on feature selection methods. Comput Electr Eng. 2014;40(1):16\u201328.","journal-title":"Comput Electr Eng"},{"issue":"1","key":"4461_CR33","doi-asserted-by":"publisher","first-page":"56","DOI":"10.38094\/jastt1224","volume":"1","author":"R Zebari","year":"2020","unstructured":"Zebari R, Abdulazeez A, Zeebaree D, Zebari D, Saeed J. A comprehensive review of dimensionality reduction techniques for feature selection and feature extraction. Journal of Applied Science and Technology Trends. 2020;1(1):56\u201370.","journal-title":"Journal of Applied Science and Technology Trends"},{"issue":"3","key":"4461_CR34","first-page":"211","volume":"4","author":"V Kumar","year":"2014","unstructured":"Kumar V, Minz S. Feature selection. J SmartCR. 2014;4(3):211\u201329.","journal-title":"J SmartCR"},{"issue":"1","key":"4461_CR35","first-page":"3","volume":"19","author":"B Venkatesh","year":"2019","unstructured":"Venkatesh B, Anuradha J. A review of feature selection and its methods. Cybern Inf Technol. 2019;19(1):3\u201326.","journal-title":"Cybern Inf Technol"},{"key":"4461_CR36","doi-asserted-by":"crossref","unstructured":"Jovi\u0107 A, Brki\u0107 K, Bogunovi\u0107 N, editors.A review of feature selection methods with applications. In: 2015 38th international convention on information andcommunication technology, electronics and microelectronics (MIPRO).Ieee,2015. p.1200-1205.","DOI":"10.1109\/MIPRO.2015.7160458"},{"issue":"6","key":"4461_CR37","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1177\/0272989X06295361","volume":"26","author":"AJ Vickers","year":"2006","unstructured":"Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making. 2006;26(6):565\u201374.","journal-title":"Med Decis Making"},{"issue":"1","key":"4461_CR38","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-024-02556-6","volume":"24","author":"X Bai","year":"2024","unstructured":"Bai X, Zhou Z, Zheng Z, Li Y, Liu K, Zheng Y, et al. Development and evaluation of machine learning models for predicting large-for-gestational-age newborns in women exposed to radiation prior to pregnancy. BMC Med Inform Decis Mak. 2024;24(1):174.","journal-title":"BMC Med Inform Decis Mak"},{"key":"4461_CR39","volume":"40","author":"S Saud","year":"2020","unstructured":"Saud S, Jamil B, Upadhyay Y, Irshad K. Performance improvement of empirical models for estimation of global solar radiation in India: a k-fold cross-validation approach. Sustain Energy Technol Assess. 2020;40:100768.","journal-title":"Sustain Energy Technol Assess"},{"issue":"1","key":"4461_CR40","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1016\/j.jeconom.2022.04.007","volume":"235","author":"X Zhang","year":"2023","unstructured":"Zhang X, Liu C-A. Model averaging prediction by K-fold cross-validation. J Econom. 2023;235(1):280\u2013301.","journal-title":"J Econom"},{"key":"4461_CR41","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.cmpb.2018.12.032","volume":"170","author":"C-C Wu","year":"2019","unstructured":"Wu C-C, Yeh W-C, Hsu W-D, Islam MM, Nguyen PA, Poly TN, et al. Prediction of fatty liver disease using machine learning algorithms. Comput Methods Programs Biomed. 2019;170:23\u20139.","journal-title":"Comput Methods Programs Biomed"},{"key":"4461_CR42","unstructured":"Yerramsetti V, Marchiori E, Textor J, Torcinovich A. Bachelor\u2019s Thesis Computing Science. Radboud University Nijmegen. 2022;1-70."},{"issue":"4","key":"4461_CR43","doi-asserted-by":"publisher","first-page":"1036","DOI":"10.1016\/j.surg.2021.08.031","volume":"171","author":"T Konishi","year":"2022","unstructured":"Konishi T, Goto T, Fujiogi M, Michihata N, Kumazawa R, Matsui H, et al. New machine learning scoring system for predicting postoperative mortality in gastroduodenal ulcer perforation: a study using a Japanese nationwide inpatient database. Surgery. 2022;171(4):1036\u201342.","journal-title":"Surgery"},{"issue":"4","key":"4461_CR44","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1007\/s40846-021-00608-0","volume":"41","author":"H-H Yen","year":"2021","unstructured":"Yen H-H, Wu P-Y, Su P-Y, Yang C-W, Chen Y-Y, Chen M-F, et al. Performance comparison of the deep learning and the human endoscopist for bleeding peptic ulcer disease. J Med Biol Eng. 2021;41(4):504\u201313.","journal-title":"J Med Biol Eng"},{"issue":"7","key":"4461_CR45","doi-asserted-by":"publisher","first-page":"912","DOI":"10.1111\/apt.15145","volume":"49","author":"GL-H Wong","year":"2019","unstructured":"Wong GL-H, Ma AJ, Deng H, Ching JY-L, Wong VW-S, Tse Y-K, et al. Machine learning model to predict recurrent ulcer bleeding in patients with history of idiopathic gastroduodenal ulcer bleeding. Aliment Pharmacol Ther. 2019;49(7):912\u20138.","journal-title":"Aliment Pharmacol Ther"},{"issue":"8","key":"4461_CR46","doi-asserted-by":"publisher","first-page":"864","DOI":"10.1111\/apt.16272","volume":"53","author":"WK Leung","year":"2021","unstructured":"Leung WK, Cheung KS, Li B, Law SYK, Lui TKL. Applications of machine learning models in the prediction of gastric cancer risk in patients after Helicobacter pylori eradication. Aliment Pharmacol Ther. 2021;53(8):864\u201372.","journal-title":"Aliment Pharmacol Ther"}],"container-title":["BMC Gastroenterology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12876-025-04461-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12876-025-04461-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12876-025-04461-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T14:47:25Z","timestamp":1765810045000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s12876-025-04461-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,15]]},"references-count":46,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["4461"],"URL":"https:\/\/doi.org\/10.1186\/s12876-025-04461-2","relation":{},"ISSN":["1471-230X"],"issn-type":[{"value":"1471-230X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,15]]},"assertion":[{"value":"26 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 December 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study strictly adhered to the ethical standards outlined in the Declaration of Helsinki. Informed consent was obtained from all subjects and\/or their legal guardian(s). Ethical approval was obtained from the ethics committee of Tehran University of Medical Sciences (TUMS)- ethics committee number is 1398-270-F-3-98.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"861"}}