Optimization of Random Forest Algorithm for Breast Cancer Detection

Authors

  • Sarika Chaudhary Assistant Professor, Department of Computer Science and Engineering, Amity University, Gurugram, INDIA Author
  • Yojna Arora Assistant Professor, Department of Computer Science and Engineering, Amity University, Gurugram, INDIA, Author
  • Neelam Yadav Department of Computer Science and Engineering, Amity University, Gurugram, INDIA, Author

Keywords:

Anaconda, breast cancer, benign, malignant, machine learning, random forest algorithm, Spider, tumor

Abstract

Today, cancer is a big issue and the most  common disease all over the world. Cancer starts due to the  abnormal growth of cells in your body. So, cancer takes  place anywhere in the body. There are more than 100 types  of cancers. The most common cancers are blood cancer,  skin cancer, lung cancer, breast cancer etc. Nowadays,  women can die because of breast cancer. There are several  techniques like machine learning algorithms, big data &  hadoop algorithms, and data mining algorithms to  addressing breast cancer. Many techniques claim that their  results were faster and more accurate. This paper presents  an optimized random forest algorithm for cancer detection. Experimental results show that random forest gives the  accuracy of 98.60%. All experiments are executed within anaconda package the scientific python/R development  environment and spider software. 

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References

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Published

2020-05-05

Issue

Vol. 8 No. 3 (2020): International Journal of Innovative Research in Computer Science and Technology (May) 2020

Section

Articles

How to Cite

Optimization of Random Forest Algorithm for Breast Cancer Detection . (2020). International Journal of Innovative Research in Computer Science & Technology, 8(3), 63–66. Retrieved from https://acspublisher.com/journals/index.php/ijircst/article/view/13264