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This study evaluates the effectiveness of cold\u2010deck, mean, and K\u2010nearest neighbor (KNN) imputation techniques for predicting thyroid disease using a dataset of 9172 observations with 31 clinical features (5.2% missing values). Feature importance analysis identified thyroid\u2010stimulating hormone (TSH), thyroxine (TT4), and free thyroxine index (FTI) as consistently significant biomarkers across all imputation methods. Five classifiers\u2014Na\u00efve Bayes, linear regression, support vector machines (SVM), LightGBM, and recurrent neural networks (RNN)\u2014were assessed on imputed datasets, with performance evaluated through accuracy, F1 score, and recall. The KNN imputation method enhanced LightGBM\u2019s accuracy by 0.47% over mean imputation (99.06% vs. 98.99%) and by 1.47% over cold deck (99.06% vs. 98.59%), demonstrating its superiority in preserving feature relationships and enhancing predictive power. LightGBM achieved the highest performance with KNN imputation (accuracy: 99.06%, F1: 97.57%, and recall: 97.83%), outperforming other classifiers by 2.5%\u20134.0% in accuracy. These results underscore the necessity of robust imputation techniques for reliable thyroid disease prediction. The study provides a reproducible framework for managing missing data in healthcare analytics, emphasizing the interplay between imputation, feature importance, and classifier selection to optimize diagnostic accuracy.<\/jats:p>","DOI":"10.1155\/acis\/2766701","type":"journal-article","created":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T17:14:10Z","timestamp":1742922850000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Effective Techniques for Handling Missing Values in Thyroid Disease Diagnosis: A Comparative Analysis"],"prefix":"10.1155","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4915-441X","authenticated-orcid":false,"given":"Dhekre Saber","family":"Saleh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4261-1873","authenticated-orcid":false,"given":"Mohd Shahizan","family":"Othman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8762-2884","authenticated-orcid":false,"given":"Wshyar Omar","family":"Khudhur","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5468-8662","authenticated-orcid":false,"given":"Eman Attallah","family":"Aljabarti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,3,24]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1530\/EJE-18-0551"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.3390\/cancers13215385"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1530\/EJE-16-0559"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1210\/clinem\/dgaa927"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.arth.2017.10.053"},{"key":"e_1_2_10_6_2","article-title":"WHO Classification of Tumours. 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