Breast Cancer Diagnosis via Classification Algorithms

Reihaneh Entezari

Breast Cancer Diagnosis via Classification Algorithms

Machine Learning 2018-07-05 v1 Machine Learning Applications Computation

Authors: Reihaneh Entezari

Abstract

In this paper, we analyze the Wisconsin Diagnostic Breast Cancer Data using Machine Learning classification techniques, such as the SVM, Bayesian Logistic Regression (Variational Approximation), and K-Nearest-Neighbors. We describe each model, and compare their performance through different measures. We conclude that SVM has the best performance among all other classifiers, while it competes closely with the Bayesian Logistic Regression that is ranked second best method for this dataset.

Keywords

genomics statistics cross-validation

Cite

@article{arxiv.1807.01334,
  title  = {Breast Cancer Diagnosis via Classification Algorithms},
  author = {Reihaneh Entezari},
  journal= {arXiv preprint arXiv:1807.01334},
  year   = {2018}
}

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