Analysis of Breast Cancer dataset using Supervised Machine Learning Classifiers
Parshavi Bolya1, Divya Jain2
1Parshavi Bolya, Department of Computer Science & Engineering, Techno India NJR Institute of Technology, Udaipur (Rajasthan), India.
2Divya Jain, Department of Computer Science & Engineering, Techno India NJR Institute of Technology, Udaipur (Rajasthan), India.
Manuscript received on 24 February 2020 | Revised Manuscript received on 10 March 2020 | Manuscript Published on 18 March 2020 | PP: 161-163 | Volume-8 Issue-6S March 2020 | Retrieval Number: F10300386S20/2020©BEIESP | DOI: 10.35940/ijrte.F1030.0386S20
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: We Have Extracted Our Dataset From Kaggle. Our Study Is About Breast Cancer Diagnosis Based On 31 Input Attributes To Produce One Output Attribute That Is The Type Of Breast Cancer. Our Analysis Is On Two Major Aspects That Are Malignant And Benign On The Basis Of 10 Attributes That Is Texture, Perimeter, Area, Smoothness, Compactness, Concavity, Symmetry, Fractal Dimension, Concave Points And Radius.
Keywords: Breast Cancer, Malignant, Benign, Fractal Dimension.
Scope of the Article: Machine Learning