Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Data Analysis on Hypothyroid Profiles using Machine Learning Algorithms Subject The topic of the resource Hypothyroid; K-Nearest Neighbor; Logistic Regression; Long Short-Term Memory; Random Forest; XBG Classifier Description An account of the resource Machine learning algorithms enable computers to learn from data and continuously enhance performance without explicit programming. Machine learning algorithms have significantly improved the accuracy and efficacy of thyroid diagnosis. This study identified and analysed the usefulness of several machine-learning algorithms in predicting hypothyroid profiles. The main goal of this study was to see the extent to which the algorithms adequately assessed whether a patient had hypothyroidism. Age, sex, health, pregnancy, and other factors are among the many factors considered. Extreme Gradient Boosting Classifier, Logistic Regression, Random Forest, Long-Term Memory, and K-Nearest Neighbors are some of the machine learning methods used. For this work, two datasets were used and analysed. Data on hypothyroidism was gathered via DataHub and Kaggle. These algorithms were applied to the collected data based on metrics such as Precision, Accuracy, F1 score and Recall. The findings showed that the Extreme Gradient Boosting classification method outperformed the others regarding F1 score, accuracy, precision, and recall. The research demonstrated how machine learning algorithms might predict thyroid profiles and identify thyroid-related illnesses. 2023 IEEE. Creator An entity primarily responsible for making the resource Leisha R.; Medows K.; Prathap B. Source A related resource from which the described resource is derived Proceedings of IEEE InC4 2023 - 2023 IEEE International Conference on Contemporary Computing and Communications Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/InC457730.2023.10262943" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/InC457730.2023.10262943</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174707457&amp;doi=10.1109%2FInC457730.2023.10262943&amp;partnerID=40&amp;md5=493224ea8b6fa6abc35980758a31aab4" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174707457&amp;doi=10.1109%2fInC457730.2023.10262943&amp;partnerID=40&amp;md5=493224ea8b6fa6abc35980758a31aab4</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835033577-4 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Leisha R., CHRIST (Deemed to Be University), Computer Science and Engineering, Bangalore, India; Medows K., CHRIST (Deemed to Be University), Computer Science and Engineering, Bangalore, India; Prathap B., CHRIST (Deemed to Be University), Computer Science and Engineering, Bangalore, India