Prediction of Thyroid Disease using Advanced Machine Learning

Authors

  • V Vasavi Sujatha  Assistant Professor, Department of Information Technology, Bhoj Reddy Engineering College for Women, Hyderabad, India
  • Bhupathi Harshini  Department of Information Technology, Bhoj Reddy Engineering College for Women, Hyderabad, India
  • Ankathi Chinmay  Department of Information Technology, Bhoj Reddy Engineering College for Women, Hyderabad, India

Keywords:

Machine Learning Algorithm, Thyroid disease, Support Vector Machine (SVM), K-NN, Decision Trees Prediction model system.

Abstract

Thyroid disorder leading cause of medical diagnosis and prediction development, which medical science is a complicated axiom. Thyroid gland is one of our body's main organs. Thyroid hormone secretions are responsible for regulating metabolism. Hyperthyroidism and hypothyroidism are the two prominent thyroid disorders that produce thyroid hormones for control of body metabolism. The machine learning is critical in the disease prediction process and in the study and classification models used for thyroid disease on the basis of data obtained from hospital datasets. A decent knowledge base must be ensured, built and used as a hybrid model to solve dynamic learning tasks like medical diagnosis and prediction tasks. Basic techniques of machine learning are used for the identification and inhibition of thyroid. The SVM is used to predict the approximate probability of a thyroid patient. If the patient has risk of getting thyroid our system has to give suggestions like recommending home remedies, precautions, medication etc.

References

  1. Bibi Amina Begum and Dr.Parkavi A “Prediction of thyroid disease using data mining techniques”,5th International Conference on Advanced Computing & Communication Systems (ICACCS) 2019. (references)
  2. Shaik Razia, A Comparative study of machine learning algorithms on thyroid disease prediction, International Journal of Engineering & Technology, 7 (2.8) (2018) 315-319
  3. Thyroid: https://en.wikipedia.org/wiki/Thyroid
  4. Aswathi A K and Anil Antony “An Intelligent System for thyroid disease classification and diagnosis” 2nd International Conference on Inventive Communication and Computational Technologies (ICICCT 2018) IEEE Xplore Compliant - Part Number: CFP18BAC-ART; ISBN:978-1-5386-1974- 2. (references)
  5. Shaik Razia, Swathi Prathyusha, Vamsi Krishna, Sathya Sumana “A Comparative study of Machine Learning Algorithm on thyroid disease prediction”, International Journal of Engineering & Technology, 7 (2.8) (2018) 315-319. (references)
  6. Haria Viral, More Suraksha, Patel Bijal and Patil Harshali “Thyroid Prediction System using Machine Learning Techniques”, International Journal of Scientific Research and Reviews (IJSRR) 2018, 7(4), 674-681. (references)
  7. K. Rajam, R. Jemina Priyadarsini, “A Survey on Diagnosis of Thyroid Disease Using Data Mining Techniques”, International Journal of Computer Science and Mobile Computing, IJCSMC, Vol. 5, Issue. 5, May 2016, pg.354 –358.
  8. D. T. Larose, Discovering knowledge in data: An introduction to data mining, John Wiley & Sons, (2005) 385
  9. Yadav Dhyan, Pal Saurabh, “To Generate an Ensemble Model for Women Thyroid Prediction Using Data Mining Techniques,” in Asian Pacific journal of cancer prevention, Vol. 20, Issue 4, pp.1275-1281, 2019.
  10. Tianqi Chen and Carlos Guestrin, “Xgboost: A scalable tree boosting system,” in Proceedings of the 22Nd ACM SIGKDD International Conferenceon Knowledge Discovery and Data Mining. KDD ’16, pages 785–794, NewYork, NY, USA, 2016. ACM
  11. John T. hancock, Taghi M. Khoshgoftaar, “CatBoost for Big Data : An interdisciplinary review” in Journal of Big Data, (2020) 7:94 https://doi.org/10.1186/s40537-020-00369-8
  12. Guolin Ke, Qi Meng, Thomas Finley, “LightGBM: A Highly Efficient Gradient Boosting Decision Tree”, in 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA
  13. Pratiksha Chalekar, Shanu Shroff, Siddhi Pise, SujaPanicker, “Use of k-Nearest Neighbor in Thyroid disease classification”, in International Journal of Current Engineering and Scientific Research
  14. Payam Refaeilzadeh, Lei Tang, and Huan Liu, “CrossValidation”, pages 532–538. Springer US, Boston, MA, 2009.
  15. Ozyilmaz, Lale, and Tulay Yildirim, "Diagnosis of thyroid disease using artificial neural network methods" Neural Information Processing,. Proceedings of the 9th International Conference on. Vol. 4. IEEE, 2002.

Downloads

Published

2023-04-30

Issue

Section

Research Articles

How to Cite

[1]
V Vasavi Sujatha, Bhupathi Harshini, Ankathi Chinmay, " Prediction of Thyroid Disease using Advanced Machine Learning, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 2, pp.655-660, March-April-2023.