Predicting the Thyroid Disease Using Machine Learning Techniques

Title

Predicting the Thyroid Disease Using Machine Learning Techniques

Creator

Krishnasamy L.; Aparnaa M.; Deepa Prabha G.; Kavya T.

Description

An endocrine gland that is allocated in the front of the neck is called the thyroid, which produces thyroid hormones as its main job. Thyroid hormone may be produced insufficiently or excessively as a result of its potential malfunction. There are various thyroid types including Hyperthyroidism, Hypothyroidism, Thyroid Cancer Thyroiditis, swelling of the thyroid. A goiter is an enlarged thyroid gland. When your thyroid gland produces more thyroid hormones than your body requires, you have hyperthyroidism. When the thyroid gland in our body doesnt provide enough thyroid hormones, then our body has hypothyroidism; when you have euthyroid sick, your thyroid function tests during critical illness taken in an inpatient or intensive care setting show alterations. Hypothyroid, hyperthyroid, and euthyroid conditions are expected from these thyroid conditions. The Three similarly used machine learning algorithms are: Support Vector Machine (SVM), Logistic Regression, and Random Forest methods, were evaluated from among the various machine learning techniques to forecast and evaluate their performance in terms of accuracy. Random forest can perform both regression and classification tasks. Logistic Regression is used to calculate or predict the probability of a binary (yes/no) event occurring. SVM classifiers offers great accuracy and work well with high dimensional space. A thyroid data set from Kaggle is used for this. This study has demonstrated the use of SVM, logistic regression, and random forest as classification tools, as well as the understanding of how to forecast thyroid disease. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Source

Lecture Notes in Networks and Systems, Vol-728 LNNS, pp. 49-57.

Date

2024-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Coverage

Krishnasamy L., Department of Computer Science and Engineering, School of Engineering and Technology, Christ (Deemed to be) University, Kengeri Campus, Bengaluru, 560074, India; Aparnaa M., Department of Information Technology, Kongu Engineering College, Tamil Nadu, Erode, India; Deepa Prabha G., Department of Information Technology, Kongu Engineering College, Tamil Nadu, Erode, India; Kavya T., Department of Information Technology, Kongu Engineering College, Tamil Nadu, Erode, India

Rights

Restricted Access

Relation

ISSN: 23673370; ISBN: 978-981993931-2

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-99-3932-9_6

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176016196&doi=10.1007%2f978-981-99-3932-9_6&partnerID=40&md5=22c1bbdae346d5c0024294052d29de94