[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120311-en":3,"doc-seo-120311-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120311,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",7,"Healthcare","Enhanced Diagnosis of Thyroid Diseases Through Advanced Machine Learning Methodologies","Thyroid disease is a major endocrine health concern involving thyroid gland dysfunction that disrupts human metabolism and can produce both physical and mental effects. Predominantly observed in women during their fourth or fifth decades, it also remains difficult to diagnose because symptoms overlap with other conditions and laboratory measurements of TSH, T3, and T4 may be affected by noise and variability. The study develops an automated classification system using multiple machine learning models and a deep learning model to categorize three thyroid disease types, addressing class imbalance with SMOTE oversampling and random undersampling. A web-based application is built using the best-performing model (GBC), and results show that TSH is the most indicative biomarker, with GBC achieving accuracy and F1-score of 99.76%.","Article  \nEnhanced Diagnosis of Thyroid Diseases Through Advanced Machine Learning Methodologies  \nOsasere Oture 1, Muhammad Zahid Iqbal 1, * and Xining (Ning) Wang 2  \nAcademic Editor: João Manuel R. S. Tavares  \nReceived: 27 November 2024  \nRevised: 20 April 2025  \nAccepted: 6 May 2025  \nPublished: 13 May 2025  \nCitation: Oture, O.; Iqbal, M.Z.; Wang, X. Enhanced Diagnosis of Thyroid Diseases Through Advanced Machine Learning Methodologies. Sci 2025, 7, 66. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)sci7020066  \nCopyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \n1 School of Computing, Engineering & Digital Technologies, Teesside University, Southfield Rd, Middlesbrough TS1 3BX, UK  \n2 School of Medicine, University of St. Andrews, St. Andrews KY16 9AJ, UK  \n* Correspondence: [zahid.iqbal@tees.ac.uk](zahid.iqbal@tees.ac.uk)  \nAbstract: Thyroid disease is a health concern related to the thyroid gland, which is vital for controlling the metabolism of the human body. Predominantly affecting women in their fourth or fifth decades of life, thyroid disease can result in physical and mental issues. This research focuses on improving the diagnostic process by creating a classification model that utilises various machine learning models and a deeplearning model to categorise three types of thyroid disease conditions. This research developed an automated system capable of classifying three thyroid conditions using five machine learning models and a deep learning model. Resampling techniques, such as SMOTE oversampling and Random undersampling, are utilised to correct the issue of class imbalance in the dataset. Finally, a web-based application is developed utilising the most effective model, GBC, which facilitates easy classification of thyroid diseases. The experimental analysis showed that the Gradient Boosting Classifier (GBC), using oversampling techniques, achieved the highest level of performance in classifying thyroid diseases, obtaining an accuracy and F1-Score of 99.76% . This study demonstrated that TSH was the most indicative biomarker for thyroid disease classification. The experimental results proved that the Gradient Boosting Classifier (GBC) utilising the oversampling technique achieved a superior performance compared to other classifier models, with an accuracy and F1-Score of 99.76% . This research presented insights that can assist healthcare practitioners in promptly diagnosing thyroid diseases.  \nKeywords: thyroid disease; thyroid hormones; oversampling; undersampling; flask framework  \n1. Introduction  \nThyroid diseases are among the most prevalent endocrine disorders, affecting millions of individuals globally. These conditions arise from dysfunctions in the thyroid gland—a critical regulator of metabolism, growth, and energy homeostasis through the secretion of hormones such as thyroxine (T4) and triiodothyronine (T3) . The thyroid-stimulating hormone (TSH), produced by the pituitary gland, tightly controls their production. Disruptions in this hormonal balance can lead to two primary thyroid disorders: hypothyroidism (underactive thyroid) and hyperthyroidism (overactive thyroid) . Hypothyroidism, characterised by insufficient hormone production, often results in fatigue, weight gain, and depression, whereas hyperthyroidism, marked by excessive hormone release, can cause weight loss, anxiety, and cardiovascular complications [1] .  \nThe global burden of thyroid diseases is particularly high in regions with iodine deficiency [2], as iodine is essential for thyroid hormone synthesis. However, even in iodine-sufficient areas, autoimmune disorders such as Hashimoto’s thyroiditis (leading to  \nhypo","cbCaiilJcZ7mPDvB","https://ap.wps.com/l/cbCaiilJcZ7mPDvB","pdf",3370497,1,15,"English","en",105,"# Introduction\n# Related Work","[{\"question\":\"What problem does the research target in thyroid disease diagnosis?\",\"answer\":\"It targets diagnostic inefficiencies caused by overlapping symptoms and limitations in blood-test-based measurement of TSH, T3, and T4, which can lead to misdiagnosis or delayed detection.\"},{\"question\":\"How is class imbalance handled in the proposed classification approach?\",\"answer\":\"The study uses resampling techniques, including SMOTE oversampling and random undersampling, to correct class imbalance in the dataset.\"},{\"question\":\"Which biomarker and model show the best diagnostic performance?\",\"answer\":\"TSH is identified as the most indicative biomarker, and the Gradient Boosting Classifier (GBC) with oversampling achieves the highest performance, with accuracy and F1-score of 99.76%.\"}]","Enhanced Diagnosis of Thyroid Diseases Through Advanced Machine Learning Methodologies | 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problem does the research target in thyroid disease diagnosis?","Question",{"text":75,"@type":76},"It targets diagnostic inefficiencies caused by overlapping symptoms and limitations in blood-test-based measurement of TSH, T3, and T4, which can lead to misdiagnosis or delayed detection.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is class imbalance handled in the proposed classification approach?",{"text":80,"@type":76},"The study uses resampling techniques, including SMOTE oversampling and random undersampling, to correct class imbalance in the dataset.",{"name":82,"@type":73,"acceptedAnswer":83},"Which biomarker and model show the best diagnostic performance?",{"text":84,"@type":76},"TSH is identified as the most indicative biomarker, and the Gradient Boosting Classifier (GBC) with oversampling achieves the highest performance, with accuracy and F1-score of 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