Data Analysis on Hypothyroid Profiles using Machine Learning Algorithms

Title

Data Analysis on Hypothyroid Profiles using Machine Learning Algorithms

Creator

Leisha R.; Medows K.; Prathap B.

Description

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.

Source

Proceedings of IEEE InC4 2023 - 2023 IEEE International Conference on Contemporary Computing and Communications

Date

2023-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Hypothyroid; K-Nearest Neighbor; Logistic Regression; Long Short-Term Memory; Random Forest; XBG Classifier

Coverage

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

Rights

Restricted Access

Relation

ISBN: 979-835033577-4

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/InC457730.2023.10262943

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174707457&doi=10.1109%2fInC457730.2023.10262943&partnerID=40&md5=493224ea8b6fa6abc35980758a31aab4