{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T16:17:41Z","timestamp":1792599461949,"version":"4.5.0"},"reference-count":40,"publisher":"Public Library of Science (PLoS)","issue":"11","license":[{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Princess Nourah bint Abdulrahman University Researchers Supporting Project","award":["PNURSP2024R104"],"award-info":[{"award-number":["PNURSP2024R104"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>Thyroid syndrome, a complex endocrine disorder, involves the dysregulation of the thyroid gland, impacting vital physiological functions. Common causes include autoimmune disorders, iodine deficiency, and genetic predispositions. The effects of thyroid syndrome extend beyond the thyroid itself, affecting metabolism, energy levels, and overall well-being. Thyroid syndrome is associated with severe cases of thyroid dysfunction, highlighting the potentially life-threatening consequences of untreated or inadequately managed thyroid disorders. This research aims to propose an advanced meta-learning approach for the timely detection of Thyroid syndrome. We used a standard thyroid-balanced dataset containing 7,000 patient records to apply advanced machine-learning methods. We proposed a novel meta-learning model based on a unique stack of K-Neighbors (KN) and Random Forest (RF) models. Then, a meta-learning Logistic Regression (LR) model is built based on the collective experience of stacked models. For the first time, the novel proposed KRL (KN-RF-LR) method is employed for the effective diagnosis of Thyroid syndrome. Extensive research experiments illustrated that the novel proposed KRL outperformed state-of-the-art approaches, achieving an impressive performance accuracy of 98%. We vindicated the performance scores through k-fold cross-validation and enhanced performance using hyperparameter tuning. Our research revolutionized the timely detection of thyroid syndrome, contributing to the enhancement of human life by reducing thyroid mortality rates.<\/jats:p>","DOI":"10.1371\/journal.pone.0312313","type":"journal-article","created":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T17:21:49Z","timestamp":1730481709000},"page":"e0312313","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":14,"title":["A novel meta learning based stacked approach for diagnosis of thyroid syndrome"],"prefix":"10.1371","volume":"19","author":[{"given":"Muhammad Asad","family":"Abbas","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5114-4213","authenticated-orcid":true,"given":"Kashif","family":"Munir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5429-9835","authenticated-orcid":true,"given":"Ali","family":"Raza","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Madiha","family":"Amjad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5957-1383","authenticated-orcid":true,"given":"Nagwan Abdel","family":"Samee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mona M.","family":"Jamjoom","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zahid","family":"Ullah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2024,11,1]]},"reference":[{"issue":"2","key":"pone.0312313.ref001","doi-asserted-by":"crossref","first-page":"101661","DOI":"10.1016\/j.beem.2022.101661","article-title":"Genetics and epigenetics of autoimmune thyroid diseases: Translational implications","volume":"37","author":"HJ Lee","year":"2023","journal-title":"Best Practice & Research Clinical Endocrinology & Metabolism"},{"issue":"2","key":"pone.0312313.ref002","doi-asserted-by":"crossref","first-page":"101741","DOI":"10.1016\/j.beem.2023.101741","article-title":"MicroRNAs in autoimmune thyroid diseases and their role as biomarkers","volume":"37","author":"R Mart\u00ednez-Hern\u00e1ndez","year":"2023","journal-title":"Best Practice & Research Clinical Endocrinology & Metabolism"},{"issue":"8","key":"pone.0312313.ref003","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1080\/27697061.2022.2159570","article-title":"The Effects of the paleo diet on autoimmune thyroid disease: a mixed methods review","volume":"42","author":"J Hollywood","year":"2023","journal-title":"Journal of the American Nutrition Association"},{"key":"pone.0312313.ref004","doi-asserted-by":"crossref","first-page":"e1394","DOI":"10.7717\/peerj-cs.1394","article-title":"A systematic review on artificial intelligence techniques for detecting thyroid diseases","volume":"9","author":"L Aversano","year":"2023","journal-title":"PeerJ Computer Science"},{"key":"pone.0312313.ref005","doi-asserted-by":"crossref","first-page":"68127","DOI":"10.1109\/ACCESS.2023.3289511","article-title":"Thyroid Detection and Classification Using DNN Based on Hybrid Meta-Heuristic and LSTM Technique","volume":"11","author":"E Mohan","year":"2023","journal-title":"IEEE Access"},{"key":"pone.0312313.ref006","first-page":"1","article-title":"Deep Multilayer Neural Network with Weights Optimization-Based Genetic Algorithm for Predicting Hypothyroid Disease","author":"FZ El-Hassani","year":"2023","journal-title":"Arabian Journal for Science and Engineering"},{"issue":"11","key":"pone.0312313.ref007","doi-asserted-by":"crossref","first-page":"875","DOI":"10.1016\/j.eprac.2023.08.006","article-title":"Prevalence and Trends of Thyroid Disease Among Adults, 1999-2018","volume":"29","author":"X Zhang","year":"2023","journal-title":"Endocrine Practice"},{"issue":"1","key":"pone.0312313.ref008","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1007\/s13755-023-00251-w","article-title":"Thyroidkeeper: a healthcare management system for patients with thyroid diseases","volume":"11","author":"J Zhang","year":"2023","journal-title":"Health Information Science and Systems"},{"key":"pone.0312313.ref009","first-page":"1","article-title":"Application of radiomics and machine learning to thyroid diseases in nuclear medicine: a systematic review","author":"F Dondi","year":"2023","journal-title":"Reviews in Endocrine and Metabolic Disorders"},{"key":"pone.0312313.ref010","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.1016\/j.procs.2021.08.106","article-title":"Thyroid disease treatment prediction with machine learning approaches","volume":"192","author":"L Aversano","year":"2021","journal-title":"Procedia Computer Science"},{"key":"pone.0312313.ref011","doi-asserted-by":"crossref","DOI":"10.1155\/2022\/9809932","article-title":"Empirical method for thyroid disease classification using a machine learning approach","volume":"2022","author":"T Alyas","year":"2022","journal-title":"BioMed Research International"},{"key":"pone.0312313.ref012","doi-asserted-by":"crossref","first-page":"1179","DOI":"10.1007\/s00500-014-1581-5","article-title":"Thyroid disease diagnosis via hybrid architecture composing rough data sets theory and machine learning algorithms","volume":"20","author":"V Prasad","year":"2016","journal-title":"Soft Computing"},{"key":"pone.0312313.ref013","doi-asserted-by":"crossref","first-page":"79354","DOI":"10.1109\/ACCESS.2019.2923547","article-title":"Thyroid ultrasound texture classification using autoregressive features in conjunction with machine learning approaches","volume":"7","author":"P Poudel","year":"2019","journal-title":"IEEE Access"},{"issue":"11","key":"pone.0312313.ref014","doi-asserted-by":"crossref","first-page":"1940","DOI":"10.3390\/diagnostics13111940","article-title":"Ensemble Learning for Hormone Binding Protein Prediction: A Promising Approach for Early Diagnosis of Thyroid Hormone Disorders in Serum","volume":"13","author":"AH Butt","year":"2023","journal-title":"Diagnostics"},{"issue":"2","key":"pone.0312313.ref015","article-title":"DIAGNOSIS OF THYROID DISEASE USING MACHINE LEARNING TECHNIQUES","volume":"7","author":"E Savc\u0131","year":"2022","journal-title":"Journal of Modern Technology & Engineering"},{"key":"pone.0312313.ref016","doi-asserted-by":"crossref","unstructured":"Rao AR, Renuka B. A machine learning approach to predict thyroid disease at early stages of diagnosis. In: 2020 IEEE international conference for innovation in technology (INOCON). IEEE; 2020. p. 1\u20134.","DOI":"10.1109\/INOCON50539.2020.9298252"},{"key":"pone.0312313.ref017","doi-asserted-by":"crossref","unstructured":"Duggal P, Shukla S. Prediction of thyroid disorders using advanced machine learning techniques. In: 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence). IEEE; 2020. p. 670\u2013675.","DOI":"10.1109\/Confluence47617.2020.9058102"},{"issue":"6","key":"pone.0312313.ref018","doi-asserted-by":"crossref","first-page":"527","DOI":"10.3390\/medicina57060527","article-title":"Ultrasound image classification of thyroid nodules using machine learning techniques","volume":"57","author":"VV Vadhiraj","year":"2021","journal-title":"Medicina"},{"key":"pone.0312313.ref019","first-page":"e23007","article-title":"Deep learning approach for brain tumor classification using metaheuristic optimization with gene expression data","author":"AA Joshi","year":"2023","journal-title":"International Journal of Imaging Systems and Technology"},{"issue":"1","key":"pone.0312313.ref020","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1007\/s10916-023-02031-1","article-title":"Optimizing gene selection and cancer classification with hybrid sine cosine and cuckoo search algorithm","volume":"48","author":"A Yaqoob","year":"2024","journal-title":"Journal of Medical Systems"},{"key":"pone.0312313.ref021","doi-asserted-by":"crossref","unstructured":"Rahman RU, Singh K, Tomar DS, Musheer R. Building Resilient Digital Forensic Frameworks for NoSQL Database: Harnessing the Blockchain and Quantum Technology. In: Sustainable Security Practices Using Blockchain, Quantum and Post-Quantum Technologies for Real Time Applications. Springer; 2024. p. 205\u2013238.","DOI":"10.1007\/978-981-97-0088-2_11"},{"key":"pone.0312313.ref022","unstructured":"AYINDE B. Thyroid Sickness Determination;. https:\/\/www.kaggle.com\/datasets\/bidemiayinde\/thyroid-sickness-determination."},{"key":"pone.0312313.ref023","first-page":"1","article-title":"A theoretical distribution analysis of synthetic minority oversampling technique (SMOTE) for imbalanced learning","author":"D Elreedy","year":"2023","journal-title":"Machine Learning"},{"issue":"4","key":"pone.0312313.ref024","doi-asserted-by":"crossref","first-page":"e0284522","DOI":"10.1371\/journal.pone.0284522","article-title":"Predicting microbe organisms using data of living micro forms of life and hybrid microbes classifier","volume":"18","author":"A Raza","year":"2023","journal-title":"Plos one"},{"key":"pone.0312313.ref025","doi-asserted-by":"crossref","first-page":"107205","DOI":"10.1016\/j.engappai.2023.107205","article-title":"Footwear-integrated force sensing resistor sensors: A machine learning approach for categorizing lower limb disorders","volume":"127","author":"HUR Siddiqui","year":"2024","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"pone.0312313.ref026","doi-asserted-by":"crossref","first-page":"107930","DOI":"10.1109\/ACCESS.2023.3320144","article-title":"LogRF: An Approach to Human Pose Estimation Using Skeleton Landmarks for Physiotherapy Fitness Exercise Correction","volume":"11","author":"A Raza","year":"2023","journal-title":"IEEE Access"},{"key":"pone.0312313.ref027","doi-asserted-by":"crossref","first-page":"138457","DOI":"10.1109\/ACCESS.2023.3340304","article-title":"Preventing Road Accidents Through Early Detection of Driver Behavior Using Smartphone Motion Sensor Data: An Ensemble Feature Engineering Approach","volume":"11","author":"A Raza","year":"2023","journal-title":"IEEE Access"},{"key":"pone.0312313.ref028","doi-asserted-by":"crossref","first-page":"135507","DOI":"10.1109\/ACCESS.2023.3337645","article-title":"AE-Net: Novel Autoencoder-Based Deep Features for SQL Injection Attack Detection","volume":"11","author":"N Thalji","year":"2023","journal-title":"IEEE Access"},{"key":"pone.0312313.ref029","article-title":"Novel Sentiment Majority Voting Classifier and Transfer Learning-based Feature Engineering for Sentiment Analysis of Deepfake Tweets","author":"M Khalid","year":"2024","journal-title":"IEEE Access"},{"issue":"1","key":"pone.0312313.ref030","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1007\/s10916-024-02076-w","article-title":"A Novel Deep Learning Approach for Forecasting Myocardial Infarction Occurrences with Time Series Patient Data","volume":"48","author":"MS Sayed","year":"2024","journal-title":"Journal of Medical Systems"},{"key":"pone.0312313.ref031","doi-asserted-by":"crossref","first-page":"100466","DOI":"10.1016\/j.dajour.2024.100466","article-title":"An efficient artificial intelligence approach for early detection of cross-site scripting attacks","volume":"11","author":"F Younas","year":"2024","journal-title":"Decision Analytics Journal"},{"key":"pone.0312313.ref032","doi-asserted-by":"crossref","first-page":"98685","DOI":"10.1109\/ACCESS.2023.3313596","article-title":"Novel Class Probability Features for Optimizing Network Attack Detection With Machine Learning","volume":"11","author":"A Raza","year":"2023","journal-title":"IEEE Access"},{"key":"pone.0312313.ref033","doi-asserted-by":"crossref","unstructured":"Darawsheh SR, Al-Shaar AS, Haziemeh FA, Alshurideh MT. Classification Thyroid Disease Using Multinomial Logistic Regressions (LR). In: The Effect of Information Technology on Business and Marketing Intelligence Systems. Springer; 2023. p. 645\u2013659.","DOI":"10.1007\/978-3-031-12382-5_34"},{"key":"pone.0312313.ref034","doi-asserted-by":"crossref","unstructured":"Rajput G, Alashetty A. Diabetes Classification Using ML Algorithms. In: Inventive Systems and Control: Proceedings of ICISC 2023. Springer; 2023. p. 867\u2013877.","DOI":"10.1007\/978-981-99-1624-5_65"},{"key":"pone.0312313.ref035","doi-asserted-by":"crossref","unstructured":"Basha MSA, Prabhavathi C, Khangembam V, Sucharitha MM, Oveis PM. Predicting Graduate Admissions using Ensemble Machine Learning Techniques: A Comparative Study of Classifiers and Regressors. In: 2023 2nd International Conference for Innovation in Technology (INOCON). IEEE; 2023. p. 1\u20136.","DOI":"10.1109\/INOCON57975.2023.10101206"},{"key":"pone.0312313.ref036","doi-asserted-by":"crossref","first-page":"111964","DOI":"10.1016\/j.solener.2023.111964","article-title":"Predicting solar distiller productivity using an AI Approach: Modified genetic algorithm with Multi-Layer Perceptron","volume":"263","author":"E Ashraf","year":"2023","journal-title":"Solar Energy"},{"key":"pone.0312313.ref037","doi-asserted-by":"crossref","first-page":"139346","DOI":"10.1016\/j.chemosphere.2023.139346","article-title":"Characteristics estimation of natural fibre reinforced plastic composites using deep multi-layer perceptron (MLP) technique","author":"T Sathish","year":"2023","journal-title":"Chemosphere"},{"key":"pone.0312313.ref038","doi-asserted-by":"crossref","unstructured":"Demir E, Bilgin M. Sentiment Analysis from Turkish News Texts with BERT-Based Language Models and Machine Learning Algorithms. In: 2023 8th International Conference on Computer Science and Engineering (UBMK). IEEE; 2023. p. 01\u201304.","DOI":"10.1109\/UBMK59864.2023.10286719"},{"key":"pone.0312313.ref039","doi-asserted-by":"crossref","first-page":"105704","DOI":"10.1016\/j.compbiomed.2022.105704","article-title":"SAPPHIRE: A stacking-based ensemble learning framework for accurate prediction of thermophilic proteins","volume":"146","author":"P Charoenkwan","year":"2022","journal-title":"Computers in Biology and Medicine"},{"key":"pone.0312313.ref040","first-page":"11","article-title":"Large-scale comparative review and assessment of computational methods for phage virion proteins identification","volume":"21","author":"M Kabir","year":"2022","journal-title":"EXCLI journal"}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0312313","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T17:22:06Z","timestamp":1730481726000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0312313"}},"subtitle":[],"editor":[{"given":"Vijayalakshmi","family":"Kakulapati","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2024,11,1]]},"references-count":40,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2024,11,1]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0312313","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,1]]}}}