{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T14:18:22Z","timestamp":1785248302954,"version":"3.55.0"},"reference-count":46,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T00:00:00Z","timestamp":1772928000000},"content-version":"vor","delay-in-days":66,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Intelligent Systems"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:p>This study proposes a novel machine learning (ML) approach for early detection of thyroid disorders using data mining and ensemble learning techniques. By leveraging a diverse dataset\u2014including demographic details, medical history, symptoms, and diagnostic test results\u2014a high\u2010precision model is developed to predict the risk of thyroid dysfunction. The methodology integrates self\u2010adaptive stacking, weighted metafeatures, and Bayesian optimization to enhance model performance. After preprocessing, feature selection, and correlation analysis, various ensemble classifiers are trained and evaluated. Ensemble methods improve prediction accuracy by combining multiple base models, making them well suited for complex medical classification tasks. The proposed model achieved outstanding performance, with an accuracy of 99.46%, sensitivity (recall for class 1) of 99.85%, specificity (recall for class 0) of 94.83%, and an overall F1\u2010score of 99.71%. The slight variation in class\u2010wise F1\u2010scores reflects the impact of class imbalance, where metrics tend to favor the majority class. Nevertheless, these metrics collectively underscore the model\u2019s robustness in effectively identifying at\u2010risk individuals while minimizing false positives and false negatives. Heat maps and other visualization tools were used to interpret patterns and improve model transparency. The findings support the potential of data\u2010driven approaches to enhance early diagnosis, inform personalized treatment plans, and improve clinical decision\u2010making. This work not only contributes to better thyroid disease detection but also advances the development of robust, interpretable, and scalable ML models for healthcare applications.<\/jats:p>","DOI":"10.1155\/int\/6746134","type":"journal-article","created":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T06:51:24Z","timestamp":1773039084000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Predictive Analysis of Early Thyroid Disorders Using Integration of Data Mining and Ensemble Intelligence Approaches"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1849-9316","authenticated-orcid":false,"given":"Sa\u2019ed","family":"Abed","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4270-1510","authenticated-orcid":false,"given":"Sherlin","family":"Saji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4151-5708","authenticated-orcid":false,"given":"Mohammad H.","family":"Alshayeji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,3,8]]},"reference":[{"key":"e_1_2_10_1_2","unstructured":"AsifU. TangJ. andHarrerS. Ensemble Knowledge Distillation for Learning Improved and Efficient Networks Proc. 24th European Conference Artificial Intelligence (ECAI) 2020 Santiago de Compostela Spain."},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.3390\/electronics10233026"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.3934\/math.20231238"},{"key":"e_1_2_10_4_2","article-title":"Thyroid Disease Classification Using Machine Learning Algorithms","volume":"1963","author":"Salman K.","year":"2021","journal-title":"Journal of Physics: Conference Series"},{"key":"e_1_2_10_5_2","first-page":"152","article-title":"Correlation-Based Comparative Machine Learning Analysis for the Classification of Metastatic Breast Cancer Using Blood Profile","volume":"8","author":"Botlagunta M.","year":"2024","journal-title":"Eurasian Journal of Medicine and Oncology"},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.123667"},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2025.3635541"},{"key":"e_1_2_10_8_2","doi-asserted-by":"crossref","unstructured":"RincyT. N.andGuptaR. Ensemble Learning Techniques and Its Efficiency in Machine Learning: A Survey 2nd International Conference on Data Engineering and Applications (IDEA) 2020 Bhopal India 1\u20136.","DOI":"10.1109\/IDEA49133.2020.9170675"},{"key":"e_1_2_10_9_2","doi-asserted-by":"publisher","DOI":"10.3390\/make6020065"},{"key":"e_1_2_10_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2023.102283"},{"key":"e_1_2_10_11_2","doi-asserted-by":"publisher","DOI":"10.3390\/make5030061"},{"key":"e_1_2_10_12_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0295501"},{"key":"e_1_2_10_13_2","doi-asserted-by":"crossref","unstructured":"MahajanP. J.andMadheS. P. Hypo and Hyperthyroid Disorder Detection From Thermal Images Using Bayesian Classifier 2014 International Conference on Advances in Communication and Computing Technologies (ICACACT) 2015 1\u20134.","DOI":"10.1109\/EIC.2015.7230721"},{"key":"e_1_2_10_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.measen.2022.100482"},{"key":"e_1_2_10_15_2","doi-asserted-by":"publisher","DOI":"10.31557\/apjcp.2019.20.4.1275"},{"key":"e_1_2_10_16_2","doi-asserted-by":"crossref","unstructured":"AswathiA. K.andAntonyA. An Intelligent System for Thyroid Disease Classification and Diagnosis 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT) 2018 Coimbatore India 1261\u20131264.","DOI":"10.1109\/ICICCT.2018.8473349"},{"key":"e_1_2_10_17_2","doi-asserted-by":"publisher","DOI":"10.1007\/s44196-023-00388-2"},{"key":"e_1_2_10_18_2","doi-asserted-by":"publisher","DOI":"10.55145\/ajest.2025.04.01.018"},{"key":"e_1_2_10_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.measen.2022.100506"},{"key":"e_1_2_10_20_2","doi-asserted-by":"publisher","DOI":"10.21608\/ejai.2023.205554.1008"},{"key":"e_1_2_10_21_2","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/9809932"},{"key":"e_1_2_10_22_2","article-title":"Performance Evaluation of SVM and Random Forest for the Diagnosis of Thyroid Disorder","volume":"9","author":"Shivastuti H. K.","year":"2021","journal-title":"International Journal for Research in Applied Science and Engineering Technology"},{"key":"e_1_2_10_23_2","doi-asserted-by":"publisher","DOI":"10.29322\/ijsrp.10.05.2020.p101117"},{"key":"e_1_2_10_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2019.100180"},{"key":"e_1_2_10_25_2","first-page":"1493","article-title":"Building Support Vector Machines With Reduced Classifier Complexity","volume":"7","author":"Keerthi S. S.","year":"2006","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_2_10_26_2","first-page":"128","article-title":"Classification of Thyroid Disease With Feature Selection Technique","volume":"2","author":"Dewangan A. K.","year":"2016","journal-title":"International Journal of Engineering and Techniques"},{"key":"e_1_2_10_27_2","doi-asserted-by":"publisher","DOI":"10.1080\/02533839.2020.1831967"},{"key":"e_1_2_10_28_2","doi-asserted-by":"crossref","first-page":"4","DOI":"10.5120\/cae2016651990","article-title":"Predicting Thyroid Disease Using Linear Discriminant Analysis (LDA) Data Mining Technique","volume":"4","author":"Banu G. R.","year":"2016","journal-title":"Communications on Applied Electronics (CAE)"},{"key":"e_1_2_10_29_2","first-page":"25","article-title":"Ar- Ann: Incorporating Association Rule Mining in Artificial Neural Network for Thyroid Disease Knowledge Discovery and Diagnosis","volume":"47","author":"Li D.","year":"2020","journal-title":"IaenG International Journal of Computer Science"},{"key":"e_1_2_10_30_2","doi-asserted-by":"publisher","DOI":"10.3390\/sci7020066"},{"key":"e_1_2_10_31_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2012.08.028"},{"key":"e_1_2_10_32_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-014-1581-5"},{"key":"e_1_2_10_33_2","first-page":"22","article-title":"A Stacking Ensemble Model for Thyroid Disease Prediction","volume":"3","author":"Islam K. H.","year":"2024","journal-title":"Ieee CS Big Data and Computing (BDC) Symposium"},{"key":"e_1_2_10_34_2","doi-asserted-by":"publisher","DOI":"10.3390\/cancers14163914"},{"key":"e_1_2_10_35_2","first-page":"75","article-title":"Predictive Data Mining for Diagnosis of Thyroid Disease Using Neural Network","volume":"3","author":"Prerana P.","year":"2015","journal-title":"International Journal of Research in Management, Science & Technology"},{"key":"e_1_2_10_36_2","doi-asserted-by":"publisher","DOI":"10.31577\/cai_2022_1_98"},{"key":"e_1_2_10_37_2","doi-asserted-by":"publisher","DOI":"10.1007\/s44230-023-00027-1"},{"key":"e_1_2_10_38_2","first-page":"41","article-title":"Effective and Accurate Bootstrap Aggregating (Bagging) Ensemble Algorithm Model for Prediction and Classification of Hypothyroid Disease","volume":"176","author":"Awujoola A. O. J.","year":"2020","journal-title":"International Journal of Computer Application"},{"key":"e_1_2_10_39_2","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13111940"},{"key":"e_1_2_10_40_2","first-page":"13","article-title":"An Experimental Comparative Study on Thyroid Disease Diagnosis Based on Feature Subset Selection and Classification","volume":"12","author":"Kousarrizi M. R. N.","year":"2012","journal-title":"International Journal of Electrical & Computer Sciences"},{"key":"e_1_2_10_41_2","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics12061474"},{"key":"e_1_2_10_42_2","doi-asserted-by":"crossref","unstructured":"AlsaadawiM.and\u015eehirliE. The Efficiency of Ensemble Techniques in Predicting Thyroid Disorder: A Comparative Study International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) 2022 Ankara Turkey 834\u2013840.","DOI":"10.1109\/ISMSIT56059.2022.9932774"},{"key":"e_1_2_10_43_2","doi-asserted-by":"crossref","unstructured":"SchneiderB. Ja \u0308ckleD. StoffelF. DiehlA. FuchsJ. andKeimD. Visual Integration of Data and Model Space in Ensemble Learning IEEE Visualization in Data Science (VDS) 2017 Phoenix AZ 15\u201322.","DOI":"10.1109\/VDS.2017.8573444"},{"key":"e_1_2_10_44_2","doi-asserted-by":"crossref","unstructured":"TyagiA. MehraR. andSaxenaA. Interactive Thyroid Disease Prediction System Using Machine Learning Technique 5th IEEE International Conference on Parallel Distributed and Grid Computing(PDGC) 2018 Solan India 689\u2013693.","DOI":"10.1109\/PDGC.2018.8745910"},{"key":"e_1_2_10_45_2","article-title":"Feature-Weighted Linear Stacking","author":"Sill J.","year":"2009","journal-title":"Arxiv Preprint Arxiv:0911.0460"},{"key":"e_1_2_10_46_2","article-title":"Enhancing Diagnostic Precision in Thyroid Nodule Classification: A Deep Learning Approach to Automated Ultrasound Image Analysis","author":"Andrade L. J. de O.","year":"2025","journal-title":"Medrxiv Preprint"}],"container-title":["International Journal of Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/int\/6746134","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1155\/int\/6746134","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/int\/6746134","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T06:51:31Z","timestamp":1773039091000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/int\/6746134"}},"subtitle":[],"editor":[{"given":"Richard","family":"Murray","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,1]]},"references-count":46,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["10.1155\/int\/6746134"],"URL":"https:\/\/doi.org\/10.1155\/int\/6746134","archive":["Portico"],"relation":{},"ISSN":["0884-8173","1098-111X"],"issn-type":[{"value":"0884-8173","type":"print"},{"value":"1098-111X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1]]},"assertion":[{"value":"2025-10-06","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-02-06","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-03-08","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"6746134"}}