Open AccessAnalysis of Breast Cancer dataset using Supervised Machine Learning Classifiers
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.f1030.0386s20
Subject(s) - perimeter , smoothness , breast cancer , fractal dimension , artificial intelligence , computer science , pattern recognition (psychology) , basis (linear algebra) , dimension (graph theory) , fractal , texture (cosmology) , supervised learning , fractal analysis , cancer , mathematics , image (mathematics) , medicine , combinatorics , artificial neural network , geometry , mathematical analysis
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.