{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T13:04:19Z","timestamp":1776344659156,"version":"3.51.2"},"reference-count":84,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T00:00:00Z","timestamp":1773100800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T00:00:00Z","timestamp":1776297600000},"content-version":"vor","delay-in-days":37,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"IITP(Institute of Information & Communications Technology Planning & Evaluation)-ITRC (Information Technology Research Center) grant funded by the Korean government","award":["(Ministry of Science and ICT, IITP-2025-RS-2024-00437191)"],"award-info":[{"award-number":["(Ministry of Science and ICT, IITP-2025-RS-2024-00437191)"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Big Data"],"DOI":"10.1186\/s40537-025-01332-1","type":"journal-article","created":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T06:46:17Z","timestamp":1773125177000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A robust framework for clinically interpretable hypothyroidism diagnosis with Polynomial-SHAP and DeepSeqNet"],"prefix":"10.1186","volume":"13","author":[{"given":"Chukwuebuka Joseph","family":"Ejiyi","sequence":"first","affiliation":[]},{"given":"Francis Ofoma","family":"Eze","sequence":"additional","affiliation":[]},{"given":"Md Altab","family":"Hossin","sequence":"additional","affiliation":[]},{"given":"Esther Stacy E. B.","family":"Aggrey","sequence":"additional","affiliation":[]},{"given":"Makuachukwu Bennedith","family":"Ejiyi","sequence":"additional","affiliation":[]},{"given":"Dongsheng","family":"Cai","sequence":"additional","affiliation":[]},{"given":"Thomas Ugochukwu","family":"Ejiyi","sequence":"additional","affiliation":[]},{"given":"Oluwatoyosi O.","family":"Bamisile","sequence":"additional","affiliation":[]},{"given":"Ann O.","family":"Nnani","sequence":"additional","affiliation":[]},{"given":"Favour Amarachi","family":"Eya","sequence":"additional","affiliation":[]},{"given":"Qin","family":"Zhen","sequence":"additional","affiliation":[]},{"given":"Mugahed A.","family":"Al-Antari","sequence":"additional","affiliation":[]},{"given":"Yeong Hyeon","family":"Gu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,3,10]]},"reference":[{"key":"1332_CR1","doi-asserted-by":"publisher","first-page":"1347","DOI":"10.1016\/S0140-6736(24)01614-3","volume":"404","author":"PN Taylor","year":"2024","unstructured":"Taylor PN, Medici MM, Hubalewska-Dydejczyk A, Boelaert K. Hypothyroidism. Lancet. 2024;404:1347\u201364.","journal-title":"Lancet"},{"key":"1332_CR2","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1038\/nrendo.2018.18","volume":"14","author":"PN Taylor","year":"2018","unstructured":"Taylor PN, Albrecht D, Scholz A, Gutierrez-Buey G, Lazarus JH, Dayan CM et al. Global epidemiology of hyperthyroidism and hypothyroidism. Nat Rev Endocrinol. 2018;14:301\u201316.","journal-title":"Nat Rev Endocrinol"},{"key":"1332_CR3","volume":"42","author":"K-H Lin","year":"2024","unstructured":"Lin K-H, Wu J-C, Wu M-C. Outcome and incidence of hypothyroidism in low-dose radioactive iodine treatment for hyperthyroidism. Rev Espa\u00f1ola Med Nucl E Imagen Mol (English Ed). 2024;42:500056.","journal-title":"Rev Espa\u00f1ola Med Nucl e Imagen Mol (English Ed)"},{"key":"1332_CR4","doi-asserted-by":"crossref","unstructured":"Riis KR, Larsen CB, Medici BR, Jensen CZ, Winther KH, Larsen EL et al. Hypothyroid women have persistently higher oxidative stress compared to healthy controls. Eur Thyroid J. 2023;12.","DOI":"10.1530\/ETJ-23-0167"},{"key":"1332_CR5","doi-asserted-by":"publisher","first-page":"868","DOI":"10.1210\/clinem\/dgad564","volume":"109","author":"JAJ Osinga","year":"2024","unstructured":"Osinga JAJ, Derakhshan A, Feldt-Rasmussen U, Huang K, Vrijkotte TGM, M\u00e4nnist\u00f6 T, et al. TSH and FT4 reference interval recommendations and prevalence of gestational thyroid dysfunction: quantification of current diagnostic approaches. J Clin Endocrinol Metab. 2024;109:868\u201378. https:\/\/doi.org\/10.1210\/clinem\/dgad564.","journal-title":"J Clin Endocrinol Metab"},{"key":"1332_CR6","unstructured":"Patil N, Rehman A, Anastasopoulou C, Jialal I. Hypothyroidism. StatPearls; 2024. 2. https:\/\/www.ncbi.nlm.nih.gov\/books\/NBK519536\/"},{"key":"1332_CR7","doi-asserted-by":"publisher","first-page":"23","DOI":"10.2165\/00002512-200522010-00002","volume":"22","author":"P Laurberg","year":"2005","unstructured":"Laurberg P, Andersen S, Pedersen IB, Carl\u00e9 A. Hypothyroidism in the elderly: pathophysiology, diagnosis and treatment. Drugs Aging. 2005;22:23\u201338.","journal-title":"Drugs Aging"},{"key":"1332_CR8","doi-asserted-by":"publisher","first-page":"30","DOI":"10.55976\/jdh.22023116330-62","volume":"30","author":"CJ Ejiyi","year":"2023","unstructured":"Ejiyi CJ, Qin Z, Ejiyi MB, Nneji GU, Monday HN, Agu FA, et al. The internet of medical things in healthcare management: a review. J Digit Heal. 2023;30:30\u201362.","journal-title":"J Digit Heal."},{"key":"1332_CR9","doi-asserted-by":"publisher","first-page":"108847","DOI":"10.1016\/j.compbiomed.2024.108847","volume":"179","author":"CJ Ejiyi","year":"2024","unstructured":"Ejiyi CJ, Qin Z, Ejiyi MB, Ukwuoma C, Ejiyi TU, Muoka GW, et al. MACCoM: A multiple attention and convolutional cross-mixer framework for detailed 2D biomedical image segmentation. Comput Biol Med. 2024;179:108847.","journal-title":"Comput Biol Med."},{"key":"1332_CR10","doi-asserted-by":"crossref","unstructured":"Jiang W, Zhao Z. Trends in research on AI-aided drug discovery from 2009 to 2023: a 15-year bibliometric analysis. Intell Pharm. 2024.","DOI":"10.1016\/j.ipha.2024.09.001"},{"key":"1332_CR11","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1002\/biof.1995","volume":"1","author":"CJ Ejiyi","year":"2023","unstructured":"Ejiyi CJ, Qin Z, Monday H, Ejiyi MB, Ukwuoma C, Ejiyi TU, et al. Breast cancer diagnosis and management guided by data augmentation, utilizing an integrated framework of SHAP and random augmentation. Biofactors. 2023;1:21. https:\/\/doi.org\/10.1002\/biof.1995.","journal-title":"Biofactors."},{"key":"1332_CR12","doi-asserted-by":"publisher","first-page":"109708","DOI":"10.1016\/j.compbiomed.2025.109708","volume":"186","author":"CJ Ejiyi","year":"2025","unstructured":"Ejiyi CJ, Qin Z, Agbesi VK, Ejiyi MB, Chikwendu IA, Bamisile OF, et al. ATEDU-NET: An Attention-Embedded Deep Unet for multi-disease diagnosis in chest X-ray images, breast ultrasound, and retina fundus. Comput Biol Med. 2025;186:109708.","journal-title":"Comput Biol Med."},{"key":"1332_CR13","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1007\/s13748-024-00340-1","volume":"13","author":"CJ Ejiyi","year":"2024","unstructured":"Ejiyi CJ, Qin Z, Agbesi VK, Ejiyi MB, Chikwendu IA, Bamisile OF, et al. Attention-enriched deeper UNet (ADU-NET) for disease diagnosis in breast ultrasound and retina fundus images. Prog Artif Intell. 2024;13:351\u201366. https:\/\/doi.org\/10.1007\/s13748-024-00340-1.","journal-title":"Prog Artif Intell."},{"key":"1332_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.124499","volume":"255","author":"X Wang","year":"2024","unstructured":"Wang X, Wang Y. Analysis of trust factors for AI-assisted diagnosis in intelligent Healthcare: personalized management strategies in chronic disease management. Expert Syst Appl. 2024;255:124499.","journal-title":"Expert Syst Appl"},{"key":"1332_CR15","doi-asserted-by":"publisher","DOI":"10.1080\/0954898X.2024.2343341","author":"CJ Ejiyi","year":"2024","unstructured":"Ejiyi CJ, Qin Z, Nneji GU, Monday HN, Agbesi VK, Ejiyi MB, et al. Enhanced cardiovascular disease prediction modelling using machine learning techniques: a focus on cardioVitalNet. Netw Comput Neural Syst. 2024. https:\/\/doi.org\/10.1080\/0954898X.2024.2343341.","journal-title":"Netw Comput Neural Syst."},{"key":"1332_CR16","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2023.998818","volume":"17","author":"A Shalaby","year":"2023","unstructured":"Shalaby A, Soliman A, Elaskary S, Refaey A, Abdelazim M, Khalifa F. Editorial: artificial intelligence based computer-aided diagnosis applications for brain disorders from medical imaging data. Front Neurosci. 2023;17:998818.","journal-title":"Front Neurosci"},{"key":"1332_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-025-01178-7","volume":"12","author":"CJ Ejiyi","year":"2025","unstructured":"Ejiyi CJ, Cai D, Thomas D, Obiora S, Osei-Mensah E, Acen C, et al. Comprehensive review of artificial intelligence applications in renewable energy systems: current implementations and emerging trends. J Big Data. 2025;12:1\u201352. https:\/\/doi.org\/10.1186\/s40537-025-01178-7.","journal-title":"J Big Data."},{"key":"1332_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/0954898X.2024.2331506","volume":"3","author":"CJ Ejiyi","year":"2024","unstructured":"Ejiyi CJ, Qin Z, Ukwuoma CC, Nneji GU, Monday HN, Ejiyi MB, et al. Comparative performance analysis of Boruta, SHAP, and Borutashap for disease diagnosis: A study with multiple machine learning algorithms. Netw Comput Neural Syst. 2024;3:1\u201338. https:\/\/doi.org\/10.1080\/0954898X.2024.2331506.","journal-title":"Netw Comput Neural Syst."},{"key":"1332_CR19","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.infoh.2025.01.001","volume":"2","author":"M Mamun","year":"2025","unstructured":"Mamun M, Chowdhury SH, Hossain MM, Khatun MR, Iqbal S. Explainability enhanced liver disease diagnosis technique using tree selection and stacking ensemble-based random forest model. Informatics Heal. 2025;2:17\u201340.","journal-title":"Informatics Heal."},{"key":"1332_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.125928","volume":"265","author":"P Ghose","year":"2025","unstructured":"Ghose P, Oliullah K, Mahbub MK, Biswas M, Uddin KN, Jamil HM. Explainable AI assisted heart disease diagnosis through effective feature engineering and stacked ensemble learning. Expert Syst Appl. 2025;265:125928.","journal-title":"Expert Syst Appl"},{"key":"1332_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.109875","volume":"142","author":"N Petrovi\u0107","year":"2025","unstructured":"Petrovi\u0107 N, Moy\u00e0-Alcover G, Jaume-i-Cap\u00f3 A, Buades Rubio JM. Enhancing generalization in Sickle Cell Disease diagnosis through ensemble methods and feature importance analysis. Eng Appl Artif Intell. 2025;142:109875.","journal-title":"Eng Appl Artif Intell"},{"key":"1332_CR22","doi-asserted-by":"publisher","first-page":"e26647","DOI":"10.1016\/j.heliyon.2024.e26647","volume":"10","author":"TJ Maginga","year":"2024","unstructured":"Maginga TJ, Masabo E, Bakunzibake P, Kim KS, Nsenga J. Using wavelet transform and hybrid CNN-LSTM models on VOC & ultrasound IoT sensor data for non-visual maize disease detection. Heliyon. 2024;10:e26647.","journal-title":"Heliyon"},{"key":"1332_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2025.103178","volume":"65","author":"X Hu","year":"2025","unstructured":"Hu X, Yu S, Zheng J, Fang Z, Zhao Z, Qu X. A hybrid CNN-LSTM model for involuntary fall detection using wrist-worn sensors. Adv Eng Inform. 2025;65:103178.","journal-title":"Adv Eng Inform"},{"key":"1332_CR24","doi-asserted-by":"publisher","first-page":"1163","DOI":"10.1016\/j.egyr.2023.05.229","volume":"9","author":"O Bamisile","year":"2023","unstructured":"Bamisile O, Cai D, Adun H, Ejiyi C, Alowolodu O, Ezurike B, et al. Deep hybrid neural net (DHN-Net) for minute-level day-ahead solar and wind power forecast in a decarbonized power system. Energy Rep. 2023;9:1163\u201372.","journal-title":"Energy Rep"},{"key":"1332_CR25","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1053\/j.jepm.2023.02.004","volume":"45","author":"M J\u00e4hnig","year":"2023","unstructured":"J\u00e4hnig M, Th\u00f6le M, Brezina T, Schmicke M, Fehr M. Generation of de novo reference values for the thyroid hormones TT4, fT4 and TSH in healthy pet rabbits (Oryctolagus cuniculus) and healthy pet Guinea pigs (Cavia porcellus) in conjunction with a TRH-stimulation test. J Exot Pet Med. 2023;45:14\u201320.","journal-title":"J Exot Pet Med"},{"key":"1332_CR26","doi-asserted-by":"publisher","first-page":"1007","DOI":"10.1016\/j.eprac.2023.09.003","volume":"29","author":"JE Quiroz-Aldave","year":"2023","unstructured":"Quiroz-Aldave JE, Concepci\u00f3n-Zavaleta MJ, Durand-V\u00e1squez MdelC, Concepci\u00f3n-Urteaga LA, Gamarra-Osorio ER, Su\u00e1rez-Rojas J, et al. Refractory hypothyroidism: unraveling the complexities of diagnosis and management. Endocr Pract. 2023;29:1007\u201316.","journal-title":"Endocr Pract"},{"key":"1332_CR27","doi-asserted-by":"crossref","unstructured":"Campi I, Dell\u2019Acqua M, Grassi ES, Vigone MC, Persani L. Unusual causes of hyperthyrotropinemia and differential diagnosis of primary hypothyroidism: a revised diagnostic flowchart. Eur Thyroid J. 2023;12.","DOI":"10.1530\/ETJ-23-0012"},{"key":"1332_CR28","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.ejim.2024.03.013","volume":"126","author":"M Teliti","year":"2024","unstructured":"Teliti M, Fanfulla F, Croce L, Coperchini F, Rotondi M. The interplay between subclinical hypothyroidism and poor sleep quality: a systematic review. Eur J Intern Med. 2024;126:49\u201355.","journal-title":"Eur J Intern Med."},{"key":"1332_CR29","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1016\/j.ecl.2024.05.010","volume":"53","author":"S Maraka","year":"2024","unstructured":"Maraka S, Dosiou C. Subclinical hypothyroidism and thyroid autoimmunity in pregnancy: to treat or not to treat. Endocrinol Metab Clin North Am. 2024;53:363\u201376.","journal-title":"Endocrinol Metab Clin North Am"},{"key":"1332_CR30","doi-asserted-by":"publisher","first-page":"324","DOI":"10.1016\/S0026-0495(99)90080-X","volume":"48","author":"RA Feelders","year":"1999","unstructured":"Feelders RA, Swaak AJG, Romijn JA, Eggermont AMM, Tielens ET, Vreugdenhil G, et al. Characteristics of recovery from the euthyroid sick syndrome induced by tumor necrosis factor alpha in cancer patients. Metabolism. 1999;48:324\u20139.","journal-title":"Metabolism"},{"key":"1332_CR31","doi-asserted-by":"publisher","first-page":"1101","DOI":"10.1210\/clinem\/dgaa956","volume":"106","author":"J Skov","year":"2021","unstructured":"Skov J, Calissendorff J, Eriksson D, Magnusson P, K\u00e4mpe O, Bensing S, et al. Limited genetic overlap between overt hashimoto\u2019s thyroiditis and graves\u2019 disease in twins: A Population-based study. J Clin Endocrinol Metab. 2021;106:1101\u201310.","journal-title":"J Clin Endocrinol Metab"},{"key":"1332_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.intimp.2024.113532","volume":"143","author":"B Li","year":"2024","unstructured":"Li B, Cai Z, Zhang Y, Chen R, Tang S, Kong F, et al. Biomarkers associated with papillary thyroid carcinoma and Hashimoto\u2019s thyroiditis: bioinformatic analysis and experimental validation. Int Immunopharmacol. 2024;143:113532.","journal-title":"Int Immunopharmacol"},{"key":"1332_CR33","doi-asserted-by":"publisher","first-page":"R13","DOI":"10.1530\/EJE-21-0171","volume":"185","author":"MB Zimmermann","year":"2021","unstructured":"Zimmermann MB, Andersson M. GLOBAL ENDOCRINOLOGY: Global perspectives in endocrinology: coverage of iodized salt programs and iodine status in 2020. Eur J Endocrinol. 2021;185:R13-21. https:\/\/doi.org\/10.1530\/EJE-21-0171.","journal-title":"Eur J Endocrinol"},{"key":"1332_CR34","doi-asserted-by":"publisher","first-page":"1878","DOI":"10.1016\/S0140-6736(23)00457-9","volume":"401","author":"N Conrad","year":"2023","unstructured":"Conrad N, Misra S, Verbakel JY, Verbeke G, Molenberghs G, Taylor PN, et al. Incidence, prevalence, and co-occurrence of autoimmune disorders over time and by age, sex, and socioeconomic status: a population-based cohort study of 22\u00a0million individuals in the UK. Lancet. 2023;401:1878\u201390.","journal-title":"Lancet"},{"key":"1332_CR35","doi-asserted-by":"publisher","DOI":"10.1542\/peds.2022-057010","author":"MJ Bull","year":"2022","unstructured":"Bull MJ, Trotter T, Santoro SL, Christensen C, Grout RW. Health supervision for children and adolescents with Down syndrome. Pediatrics. 2022. https:\/\/doi.org\/10.1542\/peds.2022-057010.","journal-title":"Pediatrics"},{"key":"1332_CR36","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-17718-z","author":"W Zhou","year":"2020","unstructured":"Zhou W, Brumpton B, Kabil O, Gudmundsson J, Thorleifsson G, Weinstock J, et al. GWAS of thyroid stimulating hormone highlights pleiotropic effects and inverse association with thyroid cancer. Nat Commun. 2020. https:\/\/doi.org\/10.1038\/s41467-020-17718-z.","journal-title":"Nat Commun"},{"key":"1332_CR37","doi-asserted-by":"publisher","first-page":"619","DOI":"10.1038\/s41586-020-2436-0","volume":"584","author":"S Saevarsdottir","year":"2020","unstructured":"Saevarsdottir S, Olafsdottir TA, Ivarsdottir EV, Halldorsson GH, Gunnarsdottir K, Sigurdsson A, et al. FLT3 stop mutation increases FLT3 ligand level and risk of autoimmune thyroid disease. Nat. 2020;584:619\u201323.","journal-title":"Nat."},{"key":"1332_CR38","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1080\/03007995.2023.2165811","volume":"39","author":"B Urgatz","year":"2023","unstructured":"Urgatz B, Razvi S. Subclinical hypothyroidism, outcomes and management guidelines: a narrative review and update of recent literature. Curr Med Res Opin. 2023;39:351\u201365. https:\/\/doi.org\/10.1080\/03007995.2023.2165811.","journal-title":"Curr Med Res Opin."},{"key":"1332_CR39","doi-asserted-by":"publisher","first-page":"696","DOI":"10.1007\/s12020-021-02646-9","volume":"71","author":"A Stoupa","year":"2021","unstructured":"Stoupa A, Kariyawasam D, Muzza M, de Filippis T, Fugazzola L, Polak M, et al. New genetics in congenital hypothyroidism. Endocrine. 2021;71:696\u2013705. https:\/\/doi.org\/10.1007\/s12020-021-02646-9.","journal-title":"Endocrine."},{"key":"1332_CR40","doi-asserted-by":"publisher","first-page":"4405","DOI":"10.1245\/s10434-019-07786-x","volume":"26","author":"D Ahn","year":"2019","unstructured":"Ahn D, Lee GJ, Sohn JH. Levothyroxine Supplementation Following Hemithyroidectomy: Incidence, Risk Factors, and Characteristics. Ann Surg Oncol. 2019;26:4405\u201313. https:\/\/doi.org\/10.1245\/s10434-019-07786-x.","journal-title":"Ann Surg Oncol."},{"key":"1332_CR41","doi-asserted-by":"publisher","DOI":"10.3389\/fendo.2020.619841","volume":"11","author":"Y Dou","year":"2021","unstructured":"Dou Y, Chen Y, Hu D, Su X. The recovery of thyroid function in low-risk papillary thyroid cancer after lobectomy: a 3-year follow-up study. Front Endocrinol (Lausanne). 2021;11:619841.","journal-title":"Front Endocrinol (Lausanne)"},{"key":"1332_CR42","doi-asserted-by":"publisher","first-page":"664","DOI":"10.1111\/cen.14719","volume":"97","author":"P Perros","year":"2022","unstructured":"Perros P, Basu A, Boelaert K, Dayan C, Vaidya B, Williams GR, et al. Postradioiodine Graves\u2019 management: The PRAGMA study. Clin Endocrinol (Oxf). 2022;97:664\u201375. https:\/\/doi.org\/10.1111\/cen.14719.","journal-title":"Clin Endocrinol (Oxf)."},{"key":"1332_CR43","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1002\/jso.26983","volume":"126","author":"D Yaniv","year":"2022","unstructured":"Yaniv D, Vainer I, Amir I, Robenshtok E, Hirsch D, Watt T, et al. Quality of life following lobectomy versus total thyroidectomy is significantly related to hypothyroidism. J Surg Oncol. 2022;126:640\u20138.","journal-title":"J Surg Oncol"},{"key":"1332_CR44","doi-asserted-by":"publisher","first-page":"738","DOI":"10.3390\/cancers12030738","volume":"12","author":"RK Vaddepally","year":"2020","unstructured":"Vaddepally RK, Kharel P, Pandey R, Garje R, Chandra AB. Review of indications of FDA-approved immune checkpoint inhibitors per NCCN guidelines with the level of evidence. Cancers. 2020;12:738.","journal-title":"Cancers"},{"key":"1332_CR45","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.xjon.2024.02.009","volume":"18","author":"M Dell\u2019Aquila","year":"2024","unstructured":"Dell\u2019Aquila M, Rossi CS, Caldonazo T, Cancelli G, Harik L, Soletti GJ, et al. Subclinical hypothyroidism and clinical outcomes after cardiac surgery: a systematic review and meta-analysis. JTCVS Open. 2024;18:64\u201379.","journal-title":"JTCVS Open"},{"key":"1332_CR46","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1007\/s12020-019-02125-2","volume":"67","author":"EG Gruppen","year":"2020","unstructured":"Gruppen EG, Kootstra-Ros J, Kobold AM, Connelly MA, Touw D, Bos JHJ, et al. Cigarette smoking is associated with higher thyroid hormone and lower TSH levels: the PREVEND study. Endocrine. 2020;67:613\u201322.","journal-title":"Endocrine"},{"key":"1332_CR47","doi-asserted-by":"publisher","DOI":"10.1186\/s12902-021-00852-0","author":"X Chen","year":"2021","unstructured":"Chen X, Wang Jjuan, Yu L, Wang Hyu, Sun H. The association between BMI, smoking, drinking and thyroid disease: a cross-sectional study in Wuhan, China. BMC Endocr Disord. 2021. https:\/\/doi.org\/10.1186\/s12902-021-00852-0.","journal-title":"BMC Endocr Disord"},{"key":"1332_CR48","unstructured":"Smoking and hypothyroidism. What are the effects?. MedicalNewsToday. 2024. https:\/\/www.medicalnewstoday.com\/articles\/smoking-and-hypothyroidism. Accessed 12 Nov 2024."},{"key":"1332_CR49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-025-01152-3","volume":"12","author":"CJ Ejiyi","year":"2025","unstructured":"Ejiyi CJ, Cai D, Eze FO, Ejiyi MB, Idoko JE, Asere SK, et al. Polynomial-SHAP as a SMOTE alternative in conglomerate neural networks for realistic data augmentation in cardiovascular and breast cancer diagnosis. J Big Data. 2025;12:1\u201328. https:\/\/doi.org\/10.1186\/s40537-025-01152-3.","journal-title":"J Big Data."},{"key":"1332_CR50","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1016\/j.bbe.2024.06.001","volume":"44","author":"CJ Ejiyi","year":"2024","unstructured":"Ejiyi CJ, Qin Z, Ukwuoma C, Agbesi VK, Oluwasanmi A, Al-Antari MA, et al. A unified 2D medical image segmentation network (SegmentNet) through distance-awareness and local feature extraction. Biocybern Biomed Eng. 2024;44:431\u201349.","journal-title":"Biocybern Biomed Eng."},{"key":"1332_CR51","doi-asserted-by":"publisher","first-page":"1602","DOI":"10.3390\/diagnostics15131602","volume":"15","author":"E Yanar","year":"2025","unstructured":"Yanar E, Hardala\u00e7 F, Ayturan K. CELM: an ensemble deep learning model for early cardiomegaly diagnosis in chest radiography. Diagnostics. 2025;15:1602.","journal-title":"Diagnostics."},{"key":"1332_CR52","doi-asserted-by":"publisher","first-page":"100133","DOI":"10.1016\/j.cmpbup.2023.100133","volume":"5","author":"CJ Ejiyi","year":"2024","unstructured":"Ejiyi CJ, Qin Z, Nnani AO, Deng F, Ejiyi TU, Ejiyi MB, et al. ResfEANet: ResNet-fused external attention network for tuberculosis diagnosis using chest X-ray images. Comput Methods Programs Biomed Updat. 2024;5:100133.","journal-title":"Comput Methods Programs Biomed Updat."},{"key":"1332_CR53","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1016\/j.matcom.2024.10.001","volume":"229","author":"R Kumar","year":"2025","unstructured":"Kumar R, Dhua S. Dynamic analysis of hashimoto\u2019s thyroiditis bio-mathematical model using artificial neural network. Math Comput Simul. 2025;229:235\u201345.","journal-title":"Math Comput Simul"},{"key":"1332_CR54","volume":"24","author":"K Guleria","year":"2022","unstructured":"Guleria K, Sharma S, Kumar S, Tiwari S. Early prediction of hypothyroidism and multiclass classification using predictive machine learning and deep learning. Measurement: Sensors. 2022;24:100482.","journal-title":"Measurement: Sensors"},{"key":"1332_CR55","doi-asserted-by":"publisher","DOI":"10.1080\/0954898X.2024.2398531","author":"CJ Ejiyi","year":"2024","unstructured":"Ejiyi CJ, Qin Z, Ukwuoma CC, Nneji GU, Monday HN, Ejiyi MB, et al. Improved deep neural network (EnhanceNet) for real-time detection of some publicly prohibited items. Netw Comput Neural Syst. 2024. https:\/\/doi.org\/10.1080\/0954898X.2024.2398531.","journal-title":"Netw Comput Neural Syst."},{"key":"1332_CR56","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2025.109791","volume":"187","author":"CJ Ejiyi","year":"2025","unstructured":"Ejiyi CJ, Cai D, Fiasam DL, Adjei-Arthur B, Obiora S, Ayekai BJ, et al. Multi-modality medical image classification with ResoMergeNet for cataract, lung cancer, and breast cancer diagnosis. Comput Biol Med. 2025;187:109791.","journal-title":"Comput Biol Med"},{"key":"1332_CR57","doi-asserted-by":"publisher","first-page":"012140","DOI":"10.1088\/1742-6596\/1963\/1\/012140","volume":"1963","author":"K Salman","year":"2021","unstructured":"Salman K, Sonuc E. Thyroid Disease Classification Using Machine Learning Algorithms. J Phys Conf Ser. 2021;1963:012140. https:\/\/doi.org\/10.1088\/1742-6596\/1963\/1\/012140.","journal-title":"J Phys Conf Ser."},{"key":"1332_CR58","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s43856-022-00071-1","volume":"2","author":"M Hu","year":"2022","unstructured":"Hu M, Asami C, Iwakura H, Nakajima Y, Sema R, Kikuchi T, et al. Development and preliminary validation of a machine learning system for thyroid dysfunction diagnosis based on routine laboratory tests. Commun Med. 2022;2:1\u20138.","journal-title":"Commun Med."},{"key":"1332_CR59","first-page":"917","volume":"18","author":"S Sankar","year":"2022","unstructured":"Sankar S, Potti A, Naga Chandrika G, Ramasubbareddy S. Thyroid Disease Prediction Using XGBoost Algorithms. J Mob Multimed. 2022;18:917\u201334.","journal-title":"J Mob Multimed."},{"key":"1332_CR60","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-52385-4_25","author":"S El Emrani","year":"2024","unstructured":"El Emrani S, Abdoun O. Artificial neural network for thyroid disease diagnosis. Lect Notes Networks Syst. 2024. https:\/\/doi.org\/10.1007\/978-3-031-52385-4_25.","journal-title":"Lect Notes Networks Syst."},{"key":"1332_CR61","doi-asserted-by":"publisher","first-page":"758","DOI":"10.1007\/s12020-020-02523-x","volume":"72","author":"Y Shen","year":"2021","unstructured":"Shen Y, Lai Y, Xu D, Xu L, Song L, Zhou J, et al. Diagnosis of thyroid neoplasm using support vector machine algorithms based on platelet RNA-seq. Endocrine. 2021;72:758\u201383.","journal-title":"Endocrine."},{"key":"1332_CR62","doi-asserted-by":"crossref","unstructured":"Ejiyi CJ, Deng J, Ejiyi TU, Salako AA, Ejiyi MB, Anomihe CG. Design and development of android application for educational institutes. J Phys Ser. 2021. p. 1\u20138.","DOI":"10.1088\/1742-6596\/1769\/1\/012066"},{"key":"1332_CR63","doi-asserted-by":"crossref","unstructured":"Efrem I, Hoyvik M, Heldal I, Helgesen C, Kovari A, Katona J et al. Mobile application helps planning activities during pregnancy. In: 10th IEEE intenrational conference cognitive Infocommunications, CogInfoCom 2019\u2014proceeding; 20190 p. 339\u201342.","DOI":"10.1109\/CogInfoCom47531.2019.9089982"},{"key":"1332_CR64","doi-asserted-by":"crossref","unstructured":"Froland TH, Ersvar E, Sjeholt G, Heldal I, Freyen AH, Logeswaran S et al. MStikk-a mobile application for learning phlebotomy. In: 10th IEEE international conference cognitive Infocommunications, CogInfoCom 2019\u2014proceeding. Institute of Electrical and Electronics Engineers Inc.; 2019. p. 499\u2013506.","DOI":"10.1109\/CogInfoCom47531.2019.9089979"},{"key":"1332_CR65","doi-asserted-by":"publisher","first-page":"666","DOI":"10.1080\/1206212X.2024.2380664","volume":"46","author":"A Benedek Szabo","year":"2024","unstructured":"Benedek Szabo A, Katona J. A machine learning approach for skin lesion classification on iOS: implementing and optimizing a convolutional transfer learning model with Create ML. Int J Comput Appl. 2024;46:666\u201385. https:\/\/doi.org\/10.1080\/1206212X.2024.2380664.","journal-title":"Int J Comput Appl."},{"key":"1332_CR66","first-page":"330","volume":"12","author":"A Daehlen","year":"2022","unstructured":"Daehlen A, Heldal I, Katona J. Towards developing an immersive virtual reality applications for supporting vision screening\u2014a user study. J Appl Tech Educ Sci. 2022;12:330.","journal-title":"J Appl Tech Educ Sci."},{"key":"1332_CR67","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.109168","volume":"182","author":"CJ Ejiyi","year":"2024","unstructured":"Ejiyi CJ, Cai D, Ejiyi MB, Chikwendu IA, Coker K, Oluwasanmi A, et al. Polynomial-SHAP analysis of liver disease markers for capturing of complex feature interactions in machine learning models. Comput Biol Med. 2024;182:109168.","journal-title":"Comput Biol Med"},{"key":"1332_CR68","first-page":"75","volume":"7","author":"CJ Ejiyi","year":"2022","unstructured":"Ejiyi CJ, Qin Z, Adetunji SA, Happy MN, Nneji GU, Ukwuoma CC, et al. Comparative analysis of Building insurance prediction using some machine learning algorithms. Int J Interact Multimed Artif Intell. 2022;7:75\u201385.","journal-title":"Int J Interact Multimed Artif Intell"},{"key":"1332_CR69","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.107027","volume":"100","author":"R Tanone","year":"2025","unstructured":"Tanone R, Li LH, Saifullah S. ViT-CB: integrating hybrid Vision Transformer and CatBoost to enhanced brain tumor detection with SHAP. Biomed Signal Process Control. 2025;100:107027.","journal-title":"Biomed Signal Process Control"},{"key":"1332_CR70","doi-asserted-by":"publisher","DOI":"10.1016\/j.health.2023.100166","volume":"3","author":"CJ Ejiyi","year":"2023","unstructured":"Ejiyi CJ, Qin Z, Amos J, Ejiyi MB, Nnani A, Ejiyi TU, et al. A robust predictive diagnosis model for diabetes mellitus using Shapley-incorporated machine learning algorithms. Healthcare Analytics. 2023;3:100166.","journal-title":"Healthcare Analytics"},{"key":"1332_CR71","unstructured":"Shakir YH. Thyroid Disease Data Set | Kaggle. 2021. https:\/\/www.kaggle.com\/datasets\/yasserhessein\/thyroid-disease-data-set\/code. Accessed 29 Dec 2022."},{"key":"1332_CR72","doi-asserted-by":"publisher","first-page":"108125","DOI":"10.1016\/j.toxicon.2024.108125","volume":"251","author":"S Lisieska-\u017bo\u0142nierczyk","year":"2024","unstructured":"Lisieska-\u017bo\u0142nierczyk S, Gaj\u0119cka M, Zielonka \u0141, D\u0105browski M, Gaj\u0119cki MT. Blood levels of zearalenone, thyroid-stimulating hormone, and thyroid hormones in patients with colorectal cancer. Toxicon. 2024;251:108125.","journal-title":"Toxicon"},{"key":"1332_CR73","doi-asserted-by":"publisher","DOI":"10.1016\/j.yebeh.2024.110053","volume":"160","author":"C Liu","year":"2024","unstructured":"Liu C, Song Y, Wang X, Zhang G. Advances in serum thyroid hormone levels and seizures. Epilepsy Behav. 2024;160:110053.","journal-title":"Epilepsy Behav"},{"key":"1332_CR74","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 Extr. 2023;5:1195\u2013213.","journal-title":"Mach Learn Knowl Extr."},{"key":"1332_CR75","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1016\/j.bioana.2024.08.001","volume":"1","author":"KMM Uddin","year":"2024","unstructured":"Uddin KMM, Mamun A Al, Chakrabarti A, Mostafiz R. An ensemble machine learning-based approach to predict thyroid disease using hybrid feature selection. Biomed Anal. 2024;1:229\u201339.","journal-title":"Biomed Anal"},{"key":"1332_CR76","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106578","volume":"154","author":"B Liao","year":"2023","unstructured":"Liao B, Liang J, Guo B, Jia X, Lu J, Zhang T, et al. ILSHIP: an interpretable and predictive model for hypothyroidism. Comput Biol Med. 2023;154:106578.","journal-title":"Comput Biol Med"},{"key":"1332_CR77","first-page":"3111","volume":"79","author":"S Sankar","year":"2024","unstructured":"Sankar S, Sathyalakshmi S. A study on the explainability of thyroid cancer prediction: SHAP values and association-rule based feature integration framework. Comput Mater Contin. 2024;79:3111\u201338.","journal-title":"Comput Mater Contin."},{"key":"1332_CR78","doi-asserted-by":"publisher","DOI":"10.1016\/j.redox.2024.103470","volume":"79","author":"X Qi","year":"2025","unstructured":"Qi X, Wang S, Fang C, Jia J, Lin L, Yuan T. Machine learning and SHAP value interpretation for predicting comorbidity of cardiovascular disease and cancer with dietary antioxidants. Redox Biol. 2025;79: 103470.","journal-title":"Redox Biol"},{"key":"1332_CR79","doi-asserted-by":"publisher","first-page":"856","DOI":"10.1016\/j.bbe.2022.06.007","volume":"42","author":"Y Liu","year":"2022","unstructured":"Liu Y, Liu Z, Luo X, Zhao H. Diagnosis of parkinson\u2019s disease based on SHAP value feature selection. Biocybern Biomed Eng. 2022;42:856\u201369.","journal-title":"Biocybern Biomed Eng"},{"key":"1332_CR80","doi-asserted-by":"crossref","unstructured":"Islam SS, Haque MS, Miah MSU, Sarwar T, Bin, Nugraha R. Application of machine learning algorithms to predict the thyroid disease risk: an experimental comparative study. PeerJ Comput Sci. 2022; 8:e898.","DOI":"10.7717\/peerj-cs.898"},{"key":"1332_CR81","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-19-5191-6_22","author":"D Sengupta","year":"2023","unstructured":"Sengupta D, Mondal S, Raj A, Anand A. Binary classification of thyroid using comprehensive set of machine learning algorithms. Lect Notes Networks Syst. 2023. https:\/\/doi.org\/10.1007\/978-981-19-5191-6_22.","journal-title":"Lect Notes Networks Syst."},{"key":"1332_CR82","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-52385-4_25","author":"S El Emrani","year":"2024","unstructured":"El Emrani S, Abdoun O. Artificial neural network for thyroid disease diagnosis. Lect Notes Networks Syst. 2024. https:\/\/doi.org\/10.1007\/978-3-031-52385-4_25.","journal-title":"Lect Notes Networks Syst."},{"key":"1332_CR83","doi-asserted-by":"crossref","unstructured":"Khan T. Application of two-class neural Network-based classification model to predict the onset of thyroid disease. In: Proceedings of the confluence 2021 11th international conference cloud computing, data science, and engineering; 2021. p. 114\u20138.","DOI":"10.1109\/Confluence51648.2021.9377097"},{"key":"1332_CR84","doi-asserted-by":"publisher","first-page":"108029","DOI":"10.1016\/j.maturitas.2024.108029","volume":"186","author":"D Shah","year":"2024","unstructured":"Shah D, Yadav V, Singh UP, Sinha A, Dumka N, Banerjee R, et al. Prevalence of non-communicable chronic diseases in rural India amongst peri- and post-menopausal women: can artificial intelligence help in early identification? Maturitas. 2024;186:108029.","journal-title":"Maturitas"}],"container-title":["Journal of Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40537-025-01332-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-025-01332-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-025-01332-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T12:02:18Z","timestamp":1776340938000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s40537-025-01332-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,10]]},"references-count":84,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["1332"],"URL":"https:\/\/doi.org\/10.1186\/s40537-025-01332-1","relation":{},"ISSN":["2196-1115"],"issn-type":[{"value":"2196-1115","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,10]]},"assertion":[{"value":"3 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 March 2026","order":3,"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 no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"63"}}