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Detection of Thyroid using Different Machine Learning Approach

Bhavay Khatri

Heera Lal Public School, Delhi

11-13

Vol: 12, Issue: 4, 2022

Receiving Date: 2022-08-19 Acceptance Date:

2022-10-28

Publication Date:

2022-11-17

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http://doi.org/10.37648/ijrst.v12i04.003

Abstract

The purpose of this paper is to identify the diagnosis of thyroid disease and to classify the two possible types of thyroid disease (hypothyroidism and hyperthyroidism). Numerous machine-learning algorithms are currently being used to identify thyroid disease. Nonetheless, our goal is to implement the machine learning algorithm, which will allow for faster and more accurate diagnosis of thyroid disease and type. The project is being implemented in Python, and the platform from which the dataset was derived is Kaggle. The Kaggle dataset was trained using a variety of machine-learning techniques. In addition, we have attempted to reduce the number of disease detection parameters. You cannot miss best UK rolex replica watches with Swiss movements! Place an order online quickly!
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Keywords: thyroid; machine learning; Gradient Boosting Algorithm

References

  1. Ankita Tyagi and Ritika Mehra. (2018). 'Interactive Thyroid Disease Prediction System using Machine Leaming Techniques' published on ResearchGate.
  2. Yong Feng Wang, (2020). 'Comparison Study of Radiomics and Deep-Learning Based Methods for Thyroid Nodules Classification using Ultrasound Images' published on IEEE Access.
  3. Sunila Godara, (2018). 'Prediction of Thyroid Disease Using Machine Learning Techniques' published on IJEE.
  4. Hitesh Garg, (2013). 'Segmentation of Thyroid Gland in Ultrasound image using Neural Network' published on IEEE.
  5. Jahantigh, F.F.: Kidney diseases diagnosis by using fuzzy logic. In: 2015 International Conference on Industrial Engineering and Operations Management.
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