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 Classification of Hypothyroid Disorder using Optimized SVM Method Subject The topic of the resource Data Standardization; Hypothyroid disorder; Machine learning Methods; Optimized SVM Description An account of the resource Hypothyroidism is an endocrine disorder where the thyroid organ doesn't provide the enough amount of thyroid hormones. It is one of the common diseases found in women. Detection of hypothyroidism needs suitable diagnostic tests to encourage prompt analysis and medication. Accurate and early detection of a disease is more important and compulsory in healthcare domain to facilitate correct and prompt diagnosis and timely treatment. The information generated in healthcare domain is on large scale, crucial and complex with multiple parameters. To interpret and understand such a huge data and retrieve the accurate and relevant information from it is a tedious as well as challenging task. However, there is a need and importance to facilitate the patients with better medical solutions. This will help to reduce the cost, time and give more relief to users by applying advanced and upgraded knowledge. It will also assist to prevent the further complications. The proposed study gains the knowledge from the hypothyroid dataset to predict the level of disease. To identify the level of hypothyroid disorder, we used four classification machine learning techniques, namely KNN (K-Nearest Neighbour), SVM (Support Vector Machines), LR (Logistic Regression) and NN (Artificial Neural Network). The Experimental results compared the classification accuracy of four methods. Logistic Regression method achieved 96.08% accuracy among other three classifiers. But, SVM is found the best classifier after standardizing the data and parameter tuning with accuracy of 99.08%. 2019 IEEE. Creator An entity primarily responsible for making the resource Vairale V.S.; Shukla S. Source A related resource from which the described resource is derived Proceedings of the 2nd International Conference on Smart Systems and Inventive Technology, ICSSIT 2019, pp. 258-263. 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 2019-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICSSIT46314.2019.8987767" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICSSIT46314.2019.8987767</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080050083&amp;doi=10.1109%2FICSSIT46314.2019.8987767&amp;partnerID=40&amp;md5=96f007078b020f3d70fc3da9eb9fa39d" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080050083&amp;doi=10.1109%2fICSSIT46314.2019.8987767&amp;partnerID=40&amp;md5=96f007078b020f3d70fc3da9eb9fa39d</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-172812119-2 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 Vairale V.S., CHRIST (Deemed to Be University), Faculty of Engineering, Kengeri Campus, Bangalore, Karnataka, India; Shukla S., CHRIST (Deemed to Be University), Faculty of Engineering, Kengeri Campus, Bangalore, Karnataka, India