Hypothyroidism Disease Diagnosis by Using Machine Learning Algorithms

Authors

  • Awad Bin Naeem Department of Computer Science, NCB&E, Multan, Pakistan
  • Biswaranjan Senapati Doctor in Computer and Data Science Parker Hannifin Corp, USA
  • Alok Singh Chauhan School of Computing Science & Engineering, Galgotias University, Greater Noida, India
  • Mukta Makhija Integrated Academy of Management and Technology, Ghaziabad, India
  • Arpita Singh Integrated Academy of Management and Technology, Ghaziabad, India
  • Meghna Gupta Department of Computer Applications, ABES Engineering College, Ghaziabad, India
  • Pradeep Kumar Tiwari Dayanand Academy of Management Studies, Kanpur Nagar, India
  • Wael M. F. Abdel-Rehim Faculty of Computers and Information, Suez University, Suez, Egypt

Keywords:

Medical Data Set, Hypothyroidism Symptoms, SVM, KNN, Naive Bayes

Abstract

Hypothyroidism is recognized as one of the most dangerous medical disorders in the world, requiring pricey therapy. This kind of research supports the implementation, development, and assessment of clinical decision support systems, with accruable diagnosis as its most important component. Increasing the accuracy of ML algorithms is essential for the development of high-performance computer-aided diagnostic systems. The purpose of this research was to show how ensemble approaches performed in a medical data set, which might be utilized to produce more accurate diagnoses and so enhance the health index. Three algorithms were employed in this investigation. It delivers a substantial result by comparing several algorithms, culminating in the conclusion of the study and the attainment of its major purpose. The purpose model was evaluated utilizing secondary hypothyroid data, according to the experimental findings. In recent years, many academics in the healthcare industry have presented several sorts of mining algorithms. The technique may not be suited for many applications due to its difficulties in detecting acceptable data types. The accuracy of the SVM machine-learning classifier is 84.72% in diagnostic patients’ hypothyroidism symptoms in this research.

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References

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Published

16.07.2023

How to Cite

Naeem, A. B. ., Senapati, B. ., Chauhan, A. S. ., Makhija, M. ., Singh, A. ., Gupta, M. ., Tiwari, P. K. ., & Abdel-Rehim, W. M. F. . (2023). Hypothyroidism Disease Diagnosis by Using Machine Learning Algorithms. International Journal of Intelligent Systems and Applications in Engineering, 11(3), 368–373. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3178

Issue

Vol. 11 No. 3 (2023)

Section

Research Article

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