{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T19:11:11Z","timestamp":1773774671389,"version":"3.50.1"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,1,31]],"date-time":"2025-01-31T00:00:00Z","timestamp":1738281600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,1,31]],"date-time":"2025-01-31T00:00:00Z","timestamp":1738281600000},"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":["Discov Artif Intell"],"DOI":"10.1007\/s44163-025-00225-9","type":"journal-article","created":{"date-parts":[[2025,1,31]],"date-time":"2025-01-31T13:20:41Z","timestamp":1738329641000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Enhanced thyroid disease prediction using ensemble machine learning: a high-accuracy approach with feature selection and class balancing"],"prefix":"10.1007","volume":"5","author":[{"given":"Md. Rezaul","family":"Islam","sequence":"first","affiliation":[]},{"given":"Aniruddha Islam","family":"Chowdhury","sequence":"additional","affiliation":[]},{"given":"Sharmin","family":"Shama","sequence":"additional","affiliation":[]},{"given":"Md. Masudul Hasan","family":"Lamyea","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,31]]},"reference":[{"key":"225_CR1","unstructured":"Thyroid gland overview. https:\/\/www.endocrineweb.com\/endocrinology\/overview-thyroid (Accessed 25 Feb 2024)."},{"key":"225_CR2","unstructured":"American thyroid association. https:\/\/www.thyroid.org\/media-main\/press-room\/ (Accessed 25 Feb 2024)."},{"key":"225_CR3","doi-asserted-by":"publisher","first-page":"635","DOI":"10.4103\/ejim.ejim_22_19","volume":"31","author":"NM Rashad","year":"2019","unstructured":"Rashad NM, Samir GM. Prevalence, risks, and comorbidity of thyroid dysfunction: a cross-sectional epidemiological study. Egyptian J Int Med. 2019;31:635\u201341.","journal-title":"Egyptian J Int Med"},{"key":"225_CR4","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/978-3-030-91920-7_2","volume-title":"Peterson\u2019s Principles of Oral and Maxillofacial Surgery","author":"SM Roser","year":"2022","unstructured":"Roser SM, Bouloux GF. Medical Management and Preoperative Patient Assessment. In: Peterson\u2019s Principles of Oral and Maxillofacial Surgery. Cham: Springer International Publishing; 2022. p. 19\u201351."},{"issue":"4","key":"225_CR5","doi-asserted-by":"publisher","first-page":"693","DOI":"10.1007\/s12553-021-00555-5","volume":"11","author":"M Mirbabaie","year":"2021","unstructured":"Mirbabaie M, Stieglitz S, Frick NR. Artificial intelligence in disease diagnostics: a critical review and classification on the current state of research guiding future direction. Heal Technol. 2021;11(4):693\u2013731.","journal-title":"Heal Technol"},{"key":"225_CR6","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.nbt.2023.02.001","volume":"74","author":"A Holzinger","year":"2023","unstructured":"Holzinger A, Keiblinger K, Holub P, Zatloukal K, M\u00fcller H. AI for life: trends in artificial intelligence for biotechnology. New Biotechnol. 2023;74:16\u201324.","journal-title":"New Biotechnol"},{"issue":"16","key":"225_CR7","doi-asserted-by":"publisher","first-page":"3914","DOI":"10.3390\/cancers14163914","volume":"14","author":"R Chaganti","year":"2022","unstructured":"Chaganti R, Rustam F, De La Torre D\u00edez I, Maz\u00f3n JLV, Rodr\u00edguez CL, Ashraf I. Thyroid disease prediction using selective features and machine learning techniques. Cancers. 2022;14(16):3914.","journal-title":"Cancers"},{"issue":"2","key":"225_CR8","doi-asserted-by":"publisher","first-page":"396","DOI":"10.1021\/acs.chemrestox.0c00304","volume":"34","author":"M Garcia de Lomana","year":"2020","unstructured":"Garcia de Lomana M, Weber AG, Birk B, Landsiedel R, Achenbach J, Schleifer KJ, Mathea M, Kirchmair J. In silico models to predict the perturbation of molecular initiating events related to thyroid hormone homeostasis. Chem Res Toxicol. 2020;34(2):396\u2013411.","journal-title":"Chem Res Toxicol"},{"key":"225_CR9","doi-asserted-by":"crossref","unstructured":"Riajuliislam M, Rahim KZ, Mahmud A. Prediction of thyroid disease (hypothyroid) in early stage using feature selection and classification techniques. In\u00a02021 International conference on information and communication technology for sustainable development (ICICT4SD)\u00a0(pp. 60\u201364). IEEE. 2021.","DOI":"10.1109\/ICICT4SD50815.2021.9397052"},{"key":"225_CR10","doi-asserted-by":"crossref","unstructured":"Mishra S, Tadesse Y, Dash A, Jena L, Ranjan P. Thyroid disorder analysis using random forest classifier. In\u00a0intelligent and cloud computing: proceedings of ICICC 2019, Volume 2\u00a0(pp. 385\u2013390). Springer Singapore. 2021.","DOI":"10.1007\/978-981-15-6202-0_39"},{"key":"225_CR11","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1016\/j.ijhm.2013.06.006","volume":"35","author":"H Li","year":"2013","unstructured":"Li H, Li J, Chang PC, Sun J. Parametric prediction on default risk of Chinese listed tourism companies by using random oversampling, isomap, and locally linear embeddings on imbalanced samples. Int J Hosp Manag. 2013;35:141\u201351.","journal-title":"Int J Hosp Manag"},{"issue":"2","key":"225_CR12","doi-asserted-by":"publisher","first-page":"1921","DOI":"10.1007\/s11277-021-08974-3","volume":"122","author":"R Jha","year":"2022","unstructured":"Jha R, Bhattacharjee V, Mustafi A. Increasing the prediction accuracy for thyroid disease: a step towards better health for society. Wireless Pers Commun. 2022;122(2):1921\u201338.","journal-title":"Wireless Pers Commun"},{"key":"225_CR13","doi-asserted-by":"publisher","first-page":"3616","DOI":"10.1007\/s11227-020-03404-w","volume":"77","author":"M Hosseinzadeh","year":"2021","unstructured":"Hosseinzadeh M, Ahmed OH, Ghafour MY, Safara F, Hama HK, Ali S, Vo B, Chiang HS. A multiple multilayer perceptron neural network with an adaptive learning algorithm for thyroid disease diagnosis in the internet of medical things. J Supercomput. 2021;77:3616\u201337.","journal-title":"J Supercomput"},{"key":"225_CR14","unstructured":"Bardenet R, Brendel M, K\u00e9gl B, Sebag M. Collaborative hyperparameter tuning. In\u00a0International conference on machine learning\u00a0(pp. 199\u2013207). PMLR. 2013."},{"key":"225_CR15","doi-asserted-by":"publisher","first-page":"1937","DOI":"10.1007\/s10462-020-09896-5","volume":"54","author":"C Bent\u00e9jac","year":"2021","unstructured":"Bent\u00e9jac C, Cs\u00f6rg\u0151 A, Mart\u00ednez-Mu\u00f1oz G. A comparative analysis of gradient boosting algorithms. Artif Intell Rev. 2021;54:1937\u201367.","journal-title":"Artif Intell Rev"},{"key":"225_CR16","first-page":"1157","volume":"3","author":"I Guyon","year":"2003","unstructured":"Guyon I, Elisseeff A. An introduction to variable and feature selection. J Mach Learn Res. 2003;3:1157\u201382.","journal-title":"J Mach Learn Res"},{"key":"225_CR17","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F. Scikit-learn: machine learning in python fabian. J Mach Learn Res. 2011;12:2825.","journal-title":"J Mach Learn Res"},{"key":"225_CR18","first-page":"2825","volume":"12","author":"P Fabian","year":"2011","unstructured":"Fabian P. Scikit-learn: machine learning in Python. J Mach Learn Res. 2011;12:2825.","journal-title":"J Mach Learn Res"},{"issue":"19","key":"225_CR19","doi-asserted-by":"publisher","first-page":"2507","DOI":"10.1093\/bioinformatics\/btm344","volume":"23","author":"Y Saeys","year":"2007","unstructured":"Saeys Y, Inza I, Larranaga P. A review of feature selection techniques in bioinformatics. Bioinformatics. 2007;23(19):2507\u201317.","journal-title":"Bioinformatics"},{"issue":"10","key":"225_CR20","doi-asserted-by":"publisher","first-page":"2044","DOI":"10.1016\/j.ins.2009.12.010","volume":"180","author":"S Garc\u00eda","year":"2010","unstructured":"Garc\u00eda S, Fern\u00e1ndez A, Luengo J, Herrera F. Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power. Inf Sci. 2010;180(10):2044\u201364.","journal-title":"Inf Sci"},{"issue":"1","key":"225_CR21","doi-asserted-by":"publisher","first-page":"9809932","DOI":"10.1155\/2022\/9809932","volume":"2022","author":"T Alyas","year":"2022","unstructured":"Alyas T, Hamid M, Alissa K, Faiz T, Tabassum N, Ahmad A. Empirical method for thyroid disease classification using a machine learning approach. Biomed Res Int. 2022;2022(1):9809932.","journal-title":"Biomed Res Int"},{"issue":"1","key":"225_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.21608\/ejai.2023.205554.1008","volume":"2","author":"M Alnaggar","year":"2023","unstructured":"Alnaggar M, Handosa M, Medhat T, Rashad ZM. Thyroid disease multi-class classification based on optimized gradient boosting model. Egyptian J Artific Intell. 2023;2(1):1\u201314.","journal-title":"Egyptian J Artific Intell"},{"key":"225_CR23","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.898","volume":"8","author":"SS Islam","year":"2022","unstructured":"Islam SS, Haque MS, Miah MSU, Sarwar TB, Nugraha R. Application of machine learning algorithms to predict the thyroid disease risk: an experimental comparative study. PeerJ Comput Sci. 2022;8: e898.","journal-title":"PeerJ Comput Sci"},{"issue":"3","key":"225_CR24","doi-asserted-by":"publisher","first-page":"1195","DOI":"10.3390\/make5030061","volume":"5","author":"MH Alshayeji","year":"2023","unstructured":"Alshayeji MH. Early thyroid risk prediction by data mining and ensemble classifiers. Mach Learn Knowl Extract. 2023;5(3):1195\u2013213.","journal-title":"Mach Learn Knowl Extract"},{"issue":"28","key":"225_CR25","doi-asserted-by":"publisher","first-page":"315","DOI":"10.14419\/ijet.v7i2.8.10432","volume":"7","author":"S Razia","year":"2018","unstructured":"Razia S, Prathyusha PS, Krishna NV, Sumana NS. A Comparative study of machine learning algorithms on thyroid disease prediction. Int J Eng Technol. 2018;7(28):315\u20139.","journal-title":"Int J Eng Technol"},{"key":"225_CR26","doi-asserted-by":"publisher","first-page":"1128","DOI":"10.1007\/s11227-018-2469-4","volume":"76","author":"K Shankar","year":"2020","unstructured":"Shankar K, Lakshmanaprabu SK, Gupta D, Maseleno A, De Albuquerque VHC. Optimal feature-based multi-kernel SVM approach for thyroid disease classification. J Supercomput. 2020;76:1128\u201343.","journal-title":"J Supercomput"},{"key":"225_CR27","doi-asserted-by":"crossref","unstructured":"Das R, Saraswat S, Chandel D, Karan S, Kirar JS. An ai driven approach for multiclass hypothyroidism classification. In\u00a0International conference on advanced network technologies and intelligent computing\u00a0(pp. 319\u2013327). Cham: Springer International Publishing. 2021.","DOI":"10.1007\/978-3-030-96040-7_26"},{"key":"225_CR28","doi-asserted-by":"crossref","unstructured":"Sonu\u00e7 E. Thyroid disease classification using machine learning algorithms. In\u00a0Journal of physics: conference series\u00a0(Vol. 1963, No. 1, p. 012140). IOP Publishing. 2021.","DOI":"10.1088\/1742-6596\/1963\/1\/012140"},{"issue":"3","key":"225_CR29","first-page":"1","volume":"18","author":"S Sankar","year":"2022","unstructured":"Sankar S, Potti A, Chandrika GN, Ramasubbareddy S. Thyroid disease prediction using XGBoost algorithms. J Mob Multimed. 2022;18(3):1\u201318.","journal-title":"J Mob Multimed"}],"container-title":["Discover Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-025-00225-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44163-025-00225-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-025-00225-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,31]],"date-time":"2025-01-31T13:20:50Z","timestamp":1738329650000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44163-025-00225-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,31]]},"references-count":29,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["225"],"URL":"https:\/\/doi.org\/10.1007\/s44163-025-00225-9","relation":{},"ISSN":["2731-0809"],"issn-type":[{"value":"2731-0809","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,31]]},"assertion":[{"value":"25 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 January 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":"Not required.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval and consent for participation"}},{"value":"There are no conflicts of interest, according to the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"9"}}