Advertisement

Modified Ant Lion Optimization Algorithm for Improved Diagnosis of Thyroid Disease

  • Naman GuptaEmail author
  • Rishabh Jain
  • Deepak Gupta
  • Ashish Khanna
  • Aditya Khamparia
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1040)

Abstract

Thyroid is one of the most common diseases affecting millions of individuals across the world. According to the findings from numerous studies and surveys on thyroid disease, it is estimated that about 42 million people in India and around 20 million people in America are suffering from some form of thyroid diseases, and women make up the majority of thyroid patients among them. It is caused due to the under (Hypothyroidism) or over (Hyperthyroidism) functionality of thyroid gland, which is responsible for maintaining the metabolism of the body, and it is imperative to diagnose its effects as early as possible so that a possible cure or treatment can be performed at the earliest. This paper aims to propose a modified ant lion optimization algorithm (MALO) for improving the diagnostic accuracy of thyroid disease. The proposed MALO is employed as a feature selection method to identify the most significant set of attributes from a large pool of available attributes to improve the classification accuracy and to reduce the computational time. Feature selection is one of the most significant aspects of machine learning which is used to remove the insignificant features from a given dataset to improve the accuracy of machine learning classifiers. Three different classifiers, namely Random Forest, k-Nearest Neighbor (kNN) and Decision Tree, are used for diagnosing the thyroid disease. The experimental results indicate that MALO eliminates 71.5% insignificant features out of the total number of features. The best accuracy achieved on the reduced set of features is 95.94% with Random Forest Classifier. Also, a notable accuracy of 95.66% and 92.51% has been achieved by Decision Tree classifier and k-Nearest Neighbor classifier, respectively. Additionally, MALO has been compared with other optimized variants of evolutionary algorithms to show the effectiveness and superiority of the proposed algorithm. Hence, the experimental results indicate that the MALO significantly outperforms the other algorithms present in the literature.

Keywords

Thyroid disease Ant Lion optimization algorithm Modified Ant Lion optimization algorithm Evolutionary algorithm Feature selection Machine learning 

Notes

Acknowledgements

This article acknowledges the dataset used in this study is obtained from University of California, Irvine Machine Learning Repository. The authors declare that they have no conflicts of interest. This article does not contain any studies involving animals performed by any of the authors. This article does not contain any studies involving human participants performed by any of the authors.

References

  1. 1.
    Hall, J.E.: Guyton and Hall Textbook of Medical Physiology e-Book. Elsevier Health Sciences (2010)Google Scholar
  2. 2.
    Weetman, A.P.: Graves’ disease. N. Engl. J. Med. 343(17), 1236–1248 (2000)CrossRefGoogle Scholar
  3. 3.
    Fauci, A.S. (ed.): Harrison’s Principles of Internal Medicine, vol. 2. Mcgraw-hill, New York (1998)Google Scholar
  4. 4.
    Emary, E., Zawbaa, H.M., Hassanien, A.E.: Binary grey wolf optimization approaches for feature selection. Neurocomputing 172, 371–381 (2016)CrossRefGoogle Scholar
  5. 5.
    Mirjalili, S., Mirjalili, S.M., Yang, X.S.: Binary bat algorithm. Neural Comput. Appl. 25(3–4), 663–681 (2014)CrossRefGoogle Scholar
  6. 6.
    Jain, R., Gupta, D., Khanna, A.: Usability feature optimization using MWOA. In: International Conference on Innovative Computing and Communications. Springer, Singapore (2019)Google Scholar
  7. 7.
    Mirjalili, S.: The ant lion optimizer. Adv. Eng. Softw. 83, 80–98 (2015)CrossRefGoogle Scholar
  8. 8.
    Zawbaa, H.M., Emary, E., Parv, B.: Feature selection based on antlion optimization algorithm. In: 2015 Third World Conference on Complex Systems (WCCS). IEEE (2015)Google Scholar
  9. 9.
    Mirjalili, S., Jangir, P., Saremi, S.: Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems. Appl. Intell. 46(1), 79–95 (2017)CrossRefGoogle Scholar
  10. 10.
    Emary, E., Zawbaa, H.M., Hassanien, A.E.: Binary ant lion approaches for feature selection. Neurocomputing 213, 54–65 (2016)CrossRefGoogle Scholar
  11. 11.
    Ali, E.S., Elazim, S.M.A., Abdelaziz, A.Y.: Ant lion optimization algorithm for renewable distributed generations. Energy 116, 445–458 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Naman Gupta
    • 1
    Email author
  • Rishabh Jain
    • 1
  • Deepak Gupta
    • 1
  • Ashish Khanna
    • 1
  • Aditya Khamparia
    • 2
  1. 1.Maharaja Agrasen Institute of Technology, Guru Gobind Singh Indraprastha UniversityDelhiIndia
  2. 2.School of Computer Science and EngineeringLovely Professional UniversityPunjabIndia

Personalised recommendations