Insights into thyroid disease: Harnessing machine learning for analysis and classification of multi-label medical data
- Title
- Insights into thyroid disease: Harnessing machine learning for analysis and classification of multi-label medical data
- Creator
- Surendran S.; Salma M.U.
- Description
- Thyroid disease refers to a wide range of disorders that occur due to dysfunction of the thyroid gland, a small gland located at the base of the neck that produces thyroid hormones. Through the analysis of this comprehensive dataset, we aim to utilize machine learning (ML) techniques for the analysis and classification of thyroid data. Employing ML techniques for thyroid classification has the potential to improve diagnostic accuracy, facilitate timelier interventions, lower expenses, optimize doctor time, and foster a more personalized approach to thyroid care. The objective of this research was to conduct multiclassification, encompassing the broadest array of classes for the target variable. To address the imbalances within the dataset, we employed the Synthetic Oversampling Technique (SMOTE) as a resampling method. Specifically, classes with a minimum of 10 samples were retained, resulting in the inclusion of 19 out of the total 34 classes in the dataset. The importance of SMOTE in addressing class imbalance is examined in this chapter, with an emphasis on how it may be used to enhance classifier model performance. Moreover, we conducted a comparative analysis of Classification Models, including Random Forest, K-Nearest Neighbor, Decision Tree, SVM, Gradient Boosting, Multinomial Naive Bayes, and Logistic Regression, to assess their accuracies. Following the resampling of the dataset, the highest accuracy of 99.99% was achieved with the gradient booster. Additionally, this research incorporated the association rule technique to uncover meaningful relationships within the dataset. 2025 selection and editorial matter, Arun Kumar Rana, Vishnu Sharma, Sanjeev Kumar Rana, and Vijay Shanker Chaudhary; individual chapters, the contributors. All rights reserved.
- Source
- Evolution of Machine Learning and Internet of Things Applications in Biomedical Engineering, pp. 54-69.
- Date
- 2024-01-01
- Publisher
- CRC Press
- Coverage
- Surendran S., Christ(Deemed to Be University), Bengaluru, India; Salma M.U., Department of Statistics and Data Science, Christ(Deemed to Be University), Bengaluru, India
- Rights
- Restricted Access
- Relation
- ISBN: 978-104014934-8; 978-103275923-4
- Format
- Online
- Language
- English
- Type
- Book chapter
Collection
Citation
Surendran S.; Salma M.U., “Insights into thyroid disease: Harnessing machine learning for analysis and classification of multi-label medical data,” CHRIST (Deemed To Be University) Institutional Repository, accessed April 28, 2025, https://archives.christuniversity.in/items/show/17546.