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Prediction of Malignant and Benign Breast Cancer: A Data Mining Approach in Healthcare Applications

  • Vivek KumarEmail author
  • Brojo Kishore Mishra
  • Manuel Mazzara
  • Dang N. H. Thanh
  • Abhishek Verma
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 37)

Abstract

As much as data science is playing a pivotal role everywhere, health care also finds its prominent application. Breast Cancer is the top-rated type of cancer amongst women; which alone took away 627,000 lives. This high mortality rate due to breast cancer does need attention, for early detection so that prevention can be done in time. As a potential contributor to state-of-the-art technology development, data mining finds a multi-fold application in predicting Brest cancer. This work focuses on different classification techniques implementation for data mining in predicting malignant and benign breast cancer. Breast Cancer Wisconsin data set from the UCI repository has been used as an experimental dataset while attribute clump thickness being used as an evaluation class. The performances of these twelve algorithms: Ada Boost M1, Decision Table, J-Rip, J48, Lazy IBK, Lazy K-star, Logistics Regression, Multiclass Classifier, Multilayer–Perceptron, Naïve Bayes, Random Forest, and Random Tree is analyzed on this data set.

Keywords

Data mining Classification techniques UCI repository Breast cancer Classification algorithms 

Abbreviations

MAE

Mean absolute error

RMSE

Root mean squared error

RAE

Relative absolute error

RRSE

Root relative squared error

TP

True Positive

TN

True Negative

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.National University of Science and Technology-MiSiSMoscowRussian Federation
  2. 2.GIET UniversityGunupurIndia
  3. 3.Innopolis UniversityKazanRussian Federation
  4. 4.Hue College of IndustryHueVietnam
  5. 5.Malaviya National Institute of TechnologyJaipurIndia

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