Breast Cancer Detection using Gradient Boost Ensemble Decision Tree Classifier
S. Vahini Ezhilraman1, Sujatha Srinivasan2, G.Suseendran3

1S. Vahini Ezhilraman, Ph.D., Research Scholar, Department of Computer Science.
2Vels Institute of Science,Technology &and Applications Advanced Studies (VISTAS), SRM Institute for Chennai.
3Sujatha Srinivasan, Associate Professor, Department of Computer Science Vels Institute of Science, Technology &and Applications Advanced Studies (VISTAS), SRM Institute for Chennai.
4G.Suseendran, Assistant Professor, Department of Information Technology, School of Computing Sciences, Vels Institute of Science,Technology &and Applications Advanced Studies (VISTAS), SRM Institute for Chennai.
Manuscript received on November 22, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 2169-2173 | Volume-9 Issue-2, December, 2019. | Retrieval Number:  B3664129219/2019©BEIESP | DOI: 10.35940/ijeat.B3664.129219
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (

Abstract: Detection of any abnormalities in the human is a big challenge faced by many of the field experts. One such challenge is to detect the Breast Cancer. The prime mottobehind in making this paper is to detect the breast cancer with the help of breast images in an advanced and appropriate way. In this study, an attempt is made in such a way by applying the combination of various existing technics in the extracted breast images for getting better result in detecting the Breast Cancer. Consequently,feature extracting images are appliedusing Light gradient boosting ensemble decision tree classifier for identifying benign and malign features of an image. As a result, the normal and abnormal breast cancer image is detected by combining above applications. Besides, classification accuracy and minimize classification time metrics are also achieved more appropriately than the existing detectingtechnics.
Keywords: Gaussian training loss , Breast Cancer detection, Kullback–Leibler divergence value ,Light Gradient Boost, Base classifiers, c4.5 decision tree, Steepest Descent Function.