Thyroid Disease Prediction Using XGBoost Algorithms

Authors

  • S. Sankar Department of CSE, Sona College of Technology, Salem, India
  • Anupama Potti Department of CSE, Sree Dattha Institute of Engineering and Science, Hyderabad, India
  • G. Naga Chandrika Department of IT, VNRVJIET, Hyderabad, India https://orcid.org/0000-0002-8991-5930
  • Somula Ramasubbareddy Department of IT, VNRVJIET, Hyderabad, India https://orcid.org/0000-0002-3306-6589

DOI:

https://doi.org/10.13052/jmm1550-4646.18322

Keywords:

Thyroid disease, classification algorithm, Hormone, Machine leaning, Disease prediction

Abstract

Nowadays, thyroid disease is increasing rapidly all over the world. Significantly, one out of ten people is affected by the thyroid in India. In recent years, many researchers have done various research works on thyroid disease detection. Therefore, the early stage of thyroid disease prediction is difficult to protect and avoid the worst health condition. In this regard, the machine learning plays a crucial role to detect the disease accurately. We consider the UC Irvin knowledge discovery dataset. So, this paper proposes the XGBoost algorithm to predict thyroid disease accurately. The best features are selected using XGBoost function. The proposed XGBoost algorithm’s efficacy is compared to decision tree, logistic regression, k-Nearest Neighbor (kNN) methods. The performance of all four algorithms is compared and analyzed. It is observed that the accuracy of the XGBoost algorithm increases by 2% than the KNN algorithm.

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Author Biographies

S. Sankar, Department of CSE, Sona College of Technology, Salem, India

S. Sankar received M.E degree from Anna University and PhD degree from VIT University, Vellore, India in 2019. He is currently working as an Assistant Professor in Sona college of Technology, Salem. Research interest includes Internet of Things, Wireless Sensor Networks and Machine Learning. He has published various papers in international journals and conferences.

Anupama Potti, Department of CSE, Sree Dattha Institute of Engineering and Science, Hyderabad, India

Anupama Potti currently working as Assistant Professor in Department of Computer Science and Engineering in Sree Dattha Engineering And Science. She Completed Master’s degree from JNTU Hyderabad and Bachelor Degree from Andhra University in Computer Science and Engineering. Interested Research areas are Machine Learning, Network Security. She has 7 years of teaching experience.

G. Naga Chandrika, Department of IT, VNRVJIET, Hyderabad, India

G. Naga Chandrika received the B. Tech degree in Computer Science and Information Technology from JNTU, Hyderabad in 2002 and M. Tech degree in Software Engineering from JNTU, Hyderabad, in 2004. She is currently pursuing Ph.D degree in Computer Science and Engineering at ANU, Guntur, AP, India. Since 2011, she has been an Assistant Professor with the Information Technology Department, VNRVJIET, Hyderabad, India. Her research interests include Data Mining, Machine Learning.

Somula Ramasubbareddy, Department of IT, VNRVJIET, Hyderabad, India

Somula Ramasubbareddy is pursuing his PhD in Computer Science and Engineering (CSE), from VIT University, Vellore, India. He did his M.tech from JNTUA, Anantapur, India in 2015. His research areas are Mobile Cloud Computing, Network security, Distributed Computing, Computer Communications (Networks) and Algorithms, IOT

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Published

2022-02-04

How to Cite

Sankar, S. ., Potti, A. ., Chandrika, G. N. ., & Ramasubbareddy, S. . (2022). Thyroid Disease Prediction Using XGBoost Algorithms. Journal of Mobile Multimedia, 18(03), 917–934. https://doi.org/10.13052/jmm1550-4646.18322

Issue

Section

Enabling AI Technologies Towards Multimedia Data Analytics for Smart Healthcare

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