[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123257-en":3,"doc-seo-123257-105":29,"detail-sidebar-cat-0-en-105":90},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},123257,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Improving Accuracy for Classification of Thyroid Disorders using Machine Learning Algorithm","Thyroid disorders are prevalent endocrine conditions that require accurate identification to support timely treatment and clinical management. Conventional diagnostics, including laboratory tests and clinical evaluation, can be slow and subject to human variability. This study proposes an automated machine learning approach using the thyroidDF.csv dataset, applying models such as Random Forest, ANN, and XGBoost with preprocessing steps like feature selection, data balancing, and hyperparameter tuning. Results show XGBoost delivering the strongest accuracy, recall, and precision, while also addressing issues of class imbalance and the need for interpretability, aiming for practical reliability in medical decision support.","International Journal of Innovative Research in Science  \nEngineering and Technology (IJIRSET)  \n(A Monthly, Peer Reviewed, Refereed, Scholarly Indexed, Open Access Journal)  \nImpact Factor: 8.699 Volume 14, Issue 4 , April 2025  \n|[www.ijirset.com](www.ijirset.com |A Monthly)[ |A Monthly](www.ijirset.com |A Monthly), Peer Reviewed & Refereed Journal| e-ISSN: 2319-8753| p-ISSN: 2347-6710|  \nVolume 14, Issue 4, April 2025  \n|DOI: 10.15680/IJIRSET.2025.1404444|  \nImproving Accuracy for Classification of Thyroid Disorders using Machine Learning Algorithm  \nMrs.Poovizhi, S.Akhil, S.Chandana, M.Suvan Reddy, L.Dehith Kumar  \nAssistant professor, Dept. ofCSE, Bharath Institute of Higher Education and Research, Selaiyur,  \nChennai, India  \nB.Tech Student, Dept. ofCSE, Bharath Institute of Higher Education and Research, Selaiyur,  \nChennai, India  \nB.Tech Student, Dept. ofCSE, Bharath Institute of Higher Education and Research, Selaiyur,  \nChennai, India  \nB.Tech Student, Dept. ofCSE, Bharath Institute of Higher Education and Research, Selaiyur,  \nChennai, India  \nB.Tech Student, Dept. ofCSE, Bharath Institute of Higher Education and Research, Selaiyur,  \nChennai, India  \nABSTRACT: Thyroid disorders are common endocrine conditions that affect millions of people worldwide. Accurately identifying these disorders is essential for effective treatment and management. Traditional diagnostic methods, such as laboratory tests and clinical evaluations, are reliable but can be time-consuming and sometimes affected by human error. With advancements in artificial intelligence and machine learning (ML), automated systems are now being developed to improve diagnostic accuracy and efficiency.  \nThis study aims to build a machine learning model that classifies thyroid disorders using the thyroidDF.csv dataset. Several ML algorithms, including Random Forest, Artificial Neural Networks (ANN), and XGBoost, are applied to analyze patient data. Various data preprocessing techniques, such as feature selection, data balancing, and hyperparameter tuning, are used to enhance model performance. Among these models, XGBoost performs the best, showing higher accuracy, recall, and precision compared to others.  \nBeyond classification, this research also addresses challenges like data imbalance and the need for model interpretability in medical applications. The proposed system is designed to be practical and reliable for clinical use, helping healthcare professionals make informed decisions for early detection and diagnosis. Future improvements could include expanding the dataset, incorporating explainable AI techniques, and developing real-time monitoring features to further improve accuracy and usability in real-world settings.  \nThis report is structured into chapters covering the motivation, methodology, experimental results, and future directions of this research. By integrating AI with medical diagnostics, this study contributes to improving healthcare solutions, enabling more precise diagnoses and better treatment strategies for thyroid disorders.  \nKEYWORDS:  \nAbbreviation Full Form  \nAI Artificial Intelligence  \nANN Artificial Neural Network  \nCSV Comma Separated Values  \nDL Deep Learning  \nEHR Electronic Health Record  \nIJIRSET©2025 | An ISO 9001:2008 Certified Journal | 8947  \n|[www.ijirset.com](www.ijirset.com |A Monthly)[ |A Monthly](www.ijirset.com |A Monthly), Peer Reviewed & Refereed Journal| e-ISSN: 2319-8753| p-ISSN: 2347-6710|  \nVolume 14, Issue 4, April 2025  \n|DOI: 10.15680/IJIRSET.2025.1404444|  \nAbbreviation Full Form  \nFN False Negative  \nFP False Positive  \nFL Federated Learning  \nGDPR General Data Protection Regulation  \nGNB Gaussian Naïve Bayes  \nHIPAA Health Insurance Portability and Accountability Act  \nIoMT Internet of Medical Things  \nKNN K-Nearest Neighbors  \nLSTM Long Short-Term Memory  \nML Machine Learning  \nMSE Mean Squared Error  \nNB Naïve Bayes  \nPCA Principal Component Analysis  \nRFE Recursive Feature Elimination  \nRF Random Fo","cbCaibDAngbVpnKN","https://ap.wps.com/l/cbCaibDAngbVpnKN","pdf",1246486,1,"English","en",105,"# Introduction\n## Background\n## Problem Statement\n## Objectives\n# System Architecture\n## Introduction to Thyroid Disorders and Diagnosis","[{\"question\":\"Why is accurate classification of thyroid disorders important?\",\"answer\":\"Accurate identification enables effective treatment and management. Early diagnosis helps prevent severe complications that may occur when conditions are missed or detected late.\"},{\"question\":\"Which dataset and machine learning models are used in the study?\",\"answer\":\"The study uses the thyroidDF.csv dataset. It applies Random Forest, Artificial Neural Networks (ANN), and XGBoost to analyze patient data for classification.\"},{\"question\":\"What techniques improve model performance in this research?\",\"answer\":\"Performance is improved through preprocessing methods including feature selection, data balancing, and hyperparameter tuning. These steps help address issues such as data imbalance and optimize learning behavior.\"}]","Improving Accuracy for Classification of Thyroid Disorders using Machine Learning Algorithm | PDF",1785815522,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"improving-accuracy-for-classification-of-thyroid-disorders-using-machine-learning-algorithm","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/improving-accuracy-for-classification-of-thyroid-disorders-using-machine-learning-algorithm/123257/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Why is accurate classification of thyroid disorders important?","Question",{"text":74,"@type":75},"Accurate identification enables effective treatment and management. Early diagnosis helps prevent severe complications that may occur when conditions are missed or detected late.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which dataset and machine learning models are used in the study?",{"text":79,"@type":75},"The study uses the thyroidDF.csv dataset. It applies Random Forest, Artificial Neural Networks (ANN), and XGBoost to analyze patient data for classification.",{"name":81,"@type":72,"acceptedAnswer":82},"What techniques improve model performance in this research?",{"text":83,"@type":75},"Performance is improved through preprocessing methods including feature selection, data balancing, and hyperparameter tuning. 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