A Comparative Study of Computational Intelligence for Identification of Breast Cancer

  • Divyue SharmaEmail author
  • Parva JainEmail author
  • Dilip Kumar ChoubeyEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1240)


Breast cancer is a type of invasive cancer that occurs in women. Breast cancer accounts for 18% of all cancer related deaths among women according to World Health Organization. After Lung Cancer, breast cancer is the leading cause of death of women in India. Due to inaccessibility, especially in rural areas, it is impossible for everyone to get diagnosed in time. If breast cancer is detected at an early stage, the doctor will be aided in suggesting an efficient way to proceed with the treatment of the patient, thus reducing the mortality rate and medical expenses. So, in this paper a comparative study on machine learning and computational intelligence techniques has been performed to optimize the process and achieve better accuracy and precision. The focus of this review article is to survey several articles existing on breast cancer majorly on Wisconsin dataset which is obtained from UCI repository. This review article has been concluded with suggestions for future directions.


Machine learning Soft computing KNN CNN Naive Bayes K-means Logistic Regression SVM (Support Vector Machine) Random Forest 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.School of Computer Science and Engineering (SCOPE)Vellore Institute of TechnologyVelloreIndia

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