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Breast Cancer Classification Using Logistic Regression

EasyChair Preprint no. 10683

6 pagesDate: August 7, 2023


Breast cancer is a prevalent disease among
women, and early detection plays a vital role in effective
treatment. In this study, a logistic regression model is
developed to classify breast tumors as benign or
malignant. The Wisconsin Diagnostic Breast Cancer
dataset is utilized, consisting of various features related to
tumor characteristics. The dataset is explored, visualized,
and divided into training and testing sets. A logistic
regression model is trained and evaluated using accuracy
metrics. Finally, the trained model is used to predict the
malignancy of a given breast tumor. This study highlights
the importance of accurate breast cancer classification
and demonstrates the efficacy of logistic regression in
achieving this goal.

Keyphrases: breast cancer, Classification, data exploration, data visualization, logistic regression

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {V Viswanatha and A.C Ramachandra and Avinash Bhagat and Shashank Shekhar},
  title = {Breast Cancer Classification Using Logistic Regression},
  howpublished = {EasyChair Preprint no. 10683},

  year = {EasyChair, 2023}}
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