The Application of Data Mining Techniques to the Detection of Cancer

Authors

  • Raghavendra R. Assistant Professor, Department of Computer Science and IT, Jain(Deemed-to-be University), Bangalore-27, India
  • Neeraj Kumari Assistant Professor, College of Computing Science and Information Technology, Teerthanker Mahaveer University, Moradabad, Uttar Pradesh, India
  • Surendra Yadav Professor, Department of Computer Science & Application, Vivekananda Global University, Jaipur, India
  • Prabha Shreeraj Nair Associate Professor. & Dy. HoD, Department of Information Technology and M.Tech Integrated, Noida Institute of Engineering and Technology, Greater Noida, Uttar Pradesh, India

Keywords:

Cancer detection, data mining, histogram equalization (HE), linear discriminant analysis (LDA), Elephant herding optimized logistic regression (EHOLR)

Abstract

Cancer is one of the leading causes of mortality worldwide. In 2018, there were approximately 1,735,350 new instances of cancer identified in the United States alone, and 609,640 individuals passed away as a direct result of the disease. Cancers include skin melanoma, lung bronchus cancer, breast cancer, prostate cancer, colon and rectum cancer, bladder cancer, kidney and renal pelvis cancer, and others. Cancer has risen to prominence in the scientific community due to the wide variety of cancers and the enormous number of people it affects. There is still active research on cancer prevention and diagnostic strategies. Using data mining methods, we sought to create a reliable and workable system for cancer diagnosis. Machine learning techniques may assist professionals in creating tools that enable early cancer detection. To improve cancer diagnosis rates, this research aims to introduce a novel machine learning method called the Elephant herding optimized logistic regression (EHOLR) strategy. Histogram equalization (HE) was used for preprocessing the acquired cancer data, and linear discriminant analysis (LDA) was used to extract the data's features. Finally, cancer detection is accomplished using our recommended strategy. The effectiveness of the suggested strategy is then assessed using the performance matrix, namely accuracy, recall, and precision..

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Published

11.07.2023

How to Cite

R., R. ., Kumari, N. ., Yadav, S. ., & Nair, P. S. . (2023). The Application of Data Mining Techniques to the Detection of Cancer. International Journal of Intelligent Systems and Applications in Engineering, 11(8s), 27–34. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3017

Issue

Vol. 11 No. 8s (2023): Advances on Machine Learning and Artificial Intelligence in Computer Technology

Section

Research Article

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