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Prediction of Breast Cancer Using Extremely Randomized Clustering Forests (ERCF) Technique: Prediction of Breast Cancer

Prediction of Breast Cancer Using Extremely Randomized Clustering Forests (ERCF) Technique: Prediction of Breast Cancer

Akhil Gupta, Rohit Anand, Digvijay Pandey, Nidhi Sindhwani, Subodh Wairya, Binay Kumar Pandey, Manvinder Sharma
Copyright: © 2021 |Volume: 12 |Issue: 4 |Pages: 15
ISSN: 1947-3532|EISSN: 1947-3540|EISBN13: 9781799861751|DOI: 10.4018/IJDST.287859
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MLA

Gupta, Akhil, et al. "Prediction of Breast Cancer Using Extremely Randomized Clustering Forests (ERCF) Technique: Prediction of Breast Cancer." IJDST vol.12, no.4 2021: pp.1-15. http://doi.org/10.4018/IJDST.287859

APA

Gupta, A., Anand, R., Pandey, D., Sindhwani, N., Wairya, S., Pandey, B. K., & Sharma, M. (2021). Prediction of Breast Cancer Using Extremely Randomized Clustering Forests (ERCF) Technique: Prediction of Breast Cancer. International Journal of Distributed Systems and Technologies (IJDST), 12(4), 1-15. http://doi.org/10.4018/IJDST.287859

Chicago

Gupta, Akhil, et al. "Prediction of Breast Cancer Using Extremely Randomized Clustering Forests (ERCF) Technique: Prediction of Breast Cancer," International Journal of Distributed Systems and Technologies (IJDST) 12, no.4: 1-15. http://doi.org/10.4018/IJDST.287859

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Abstract

Breast cancer is a significant public health concern in both developed and developing countries. It is almost one in three cancers diagnosed in all women. Data mining and pattern recognition applications in conjunction have been proven to be quite useful and relevant to extract the information useful for the medical purpose. This research work reflects the work based on extremely randomized clustering forests (ERCF) technique which is nothing but a type of pattern recognition technique that may be implemented as the prediction model for breast cancer (BC). The accuracy achieved through ERCF has also been compared with that of k-NN (correlation) and k-NN (Euclidean) in this research work (where k-NN refers to k-nearest neighbours technique), and thereafter, final conclusions have been drawn depending upon the testing attributes. The results show that the accuracy of ERCF in the forecasting of breast cancer is so much larger than that of the exactness of k-NN (correlation) and k-NN (Euclidean). Hence, ERCF, a randomized technique for pattern classification, is best.

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