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An investigation on machine learning models for enhanced thyroid prediction

  • University of Bolton
    ,
  • Manchester Metropolitan University
Research Output:
Chapter in Book/Report/Conference proceeding
Conference contribution
 Peer-review

Abstract

The paper aims to enhance the prediction of thyroid diseases by optimizing the deep learning process and tuning hyperparameters. This project utilizes a dataset focused on one of the most critical issues in health care: thyroid disease diagnosis. Data preprocessing, including Z-scale normalization, was applied to reduce overfitting and ensure the significance of feature contributions. Hyperparameter tuning of machine learning (ML) techniques is primarily utilized for Recurrent Neural Networks (RNNs) to optimize training and improve model classification performance. Comparison variables are utilized in ML methods, specifically with the random forest technique, to enhance model performance and accuracy. This work showcases improvements in the analytical framework and establishes a foundation for more efficient and accurate detection of thyroid diseases.

Publication Information

Output type

Research Output:
Chapter in Book/Report/Conference proceeding
Conference contribution
 Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 511-524 (14 pages)

Publication milestones

  • Published - 06/11/2024

Publication status

Published - 06/11/2024

Publication series

  • Publication series name: Lecture Notes in Networks and Systems
    ISSN (Print): 2367-3370
    ISSN (Electronic): 2367-3389
    Volume: 1293

ISBN (Print)

9789819632466

Publication IDs

  • handle.net: 10547/626419
  • Scopus: 105006885646

Host publication title

Proceedings of Fourth International Conference on Computing and Communication Networks, ICCCN 2024

Host publication editors

  • Akshi Kumar
  • Abhishek Swaroop
  • Pancham Shukla

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