Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Breast Cancer Detection by using Supervised Learning Algorithm


Affiliations
1 Master of Computer Application, Department of Computer Application, B. S. Abdur Rahman Crescent Institute of Science and Technology, Vandalur, Chennai, Tamil Nadu, India
2 Assistant Professor, Department of Computer Application, B. S. Abdur Rahman Crescent Institute of Science and Technology, Vandalur, Chennai, Tamil Nadu, India
     

   Subscribe/Renew Journal


Breast cancer is one of the largest second causes of dead among women. So, computer aided diagnosis system will be working on mammograms. In early stage breast cancer can be identified by through CAD algorithms.But, this algorithms accuracy of existing system unsatisfied result. Later, they are using to detect this cancer like microscopic images, mammography to ultrasonography and MRI. This type of prediction gave only false prediction then it took more time and cost effects. In proposed system of in this project, it will be working on big data and machine learning based on nine features in breast cancer data set from UCI Irvine Machine Learning Repository database. Big data is used to pre-processing from various fields from datasets and used to accurate detection. Machine Learning can be used to implementing the supervised algorithm’s to detect cancer type based on benign and malignant breast masses. These algorithms will give us additional accurate results for detecting the breast cancer.

Keywords

Big Data, Breast Cancer Detection, GUI, Machine Learning
Subscription Login to verify subscription
User
Notifications
Font Size


  • I. Iliopoulos, S. D. Meo, M. Pasian, M. Zhadobov, P. Pouliguen, P. Potier, L. Perregrini, R. Sauleau, and M. Ettorre, “Enhancement of penetration of millimetre waves by field focusing applied to breast cancer detection,” IEEE Transactions on Biomedical Engineering, vol. 68, no. 3, pp. 959-966, Mar. 2021, doi: 10.1109/TBME.2020.3014277.
  • Y. Jiang, “Computer-aided diagnosis of breast cancer in mammography: Evidence and potential,” Technology in Cancer Research & Treatment, vol. 1, no. 3, pp. 211-216, Jun. 2002.
  • A. C. Tan, and D. Gilbert, “Ensemble machine learning on gene expression data for cancer classification,” Applied Bioinformatics, vol. 2, no. sp.3, pp. S75-S83, 2003,
  • D. Delen, G. Walker, and A. Kadam, “Predicting breast cancer survivability: A comparison of three data mining methods,” Artificial Intelligence in Medicine, vol. 34, no. 2, pp. 113-127, 2005.
  • S. Destounis, and A. Santacroce, “Age to begin and intervals for breast cancer screening: Balancing benefits and harms,” American Journal of Roentgenology, vol. 210, no. 2, pp. 279-284, 2018.
  • S. Sahran, A. Qasem, K. Omar, D. Albashih, A. Adam, ....., and A. Shukor, “Machine learning methods for breast cancer diagnostic,” Nov. 2018.
  • M. Tahmooresi, A. Afshar, B. B. Rad, K. B. Nowshath, and M. Bamiah, “Early detection of breast cancer using machine learning techniques,” Journal of Telecommunication, Electronic and Computer Engineering , vol. 10, pp. 21-27, 2018.
  • Q. Huqng, Y. Chen, L. Liu, D. Tao, and X. Li, “On combining biclustering mining and Adaboost breast tumour classification,” IEEE Transactions on Knowledge and Data Engineering, vol. 13, 2019.

Abstract Views: 187

PDF Views: 0




  • Breast Cancer Detection by using Supervised Learning Algorithm

Abstract Views: 187  |  PDF Views: 0

Authors

B. Abarna
Master of Computer Application, Department of Computer Application, B. S. Abdur Rahman Crescent Institute of Science and Technology, Vandalur, Chennai, Tamil Nadu, India
Sudha Rajesh
Assistant Professor, Department of Computer Application, B. S. Abdur Rahman Crescent Institute of Science and Technology, Vandalur, Chennai, Tamil Nadu, India

Abstract


Breast cancer is one of the largest second causes of dead among women. So, computer aided diagnosis system will be working on mammograms. In early stage breast cancer can be identified by through CAD algorithms.But, this algorithms accuracy of existing system unsatisfied result. Later, they are using to detect this cancer like microscopic images, mammography to ultrasonography and MRI. This type of prediction gave only false prediction then it took more time and cost effects. In proposed system of in this project, it will be working on big data and machine learning based on nine features in breast cancer data set from UCI Irvine Machine Learning Repository database. Big data is used to pre-processing from various fields from datasets and used to accurate detection. Machine Learning can be used to implementing the supervised algorithm’s to detect cancer type based on benign and malignant breast masses. These algorithms will give us additional accurate results for detecting the breast cancer.

Keywords


Big Data, Breast Cancer Detection, GUI, Machine Learning

References