Classification of Hypothyroid Disorder using Optimized SVM Method

Title

Classification of Hypothyroid Disorder using Optimized SVM Method

Creator

Vairale V.S.; Shukla S.

Description

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.

Source

Proceedings of the 2nd International Conference on Smart Systems and Inventive Technology, ICSSIT 2019, pp. 258-263.

Date

2019-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Data Standardization; Hypothyroid disorder; Machine learning Methods; Optimized SVM

Coverage

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

Rights

Restricted Access

Relation

ISBN: 978-172812119-2

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICSSIT46314.2019.8987767

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080050083&doi=10.1109%2fICSSIT46314.2019.8987767&partnerID=40&md5=96f007078b020f3d70fc3da9eb9fa39d