Authors :
Tushar Khandelwal
Volume/Issue :
Volume 7 - 2022, Issue 6 - June
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3bARENC
DOI :
https://doi.org/10.5281/zenodo.6767080
Abstract :
Breast cancer causes more death in women
and it also curable if it is early diagnosed. Hence, early
detection of cancer in women will be helpful in taking
necessary actions. In order to detect the disease
supervised machine learning techniques is discussed in
this paper. With the help of Sequential ForwardSelection
(SFS) best feature will be selected for support vector
machines (SVM) model. Wisconsin breast cancerdataset
(WBCD) is used for diagnosis of breast cancer. The SVM
result shows 96% precision because of random
permutation on the data set.
Keywords :
Sequential Forward Selection SFS; Support Vector Machine; Breast Cancer; Classification; Machine Learning; Wisconsin Breast Cancer Dataset.
Breast cancer causes more death in women
and it also curable if it is early diagnosed. Hence, early
detection of cancer in women will be helpful in taking
necessary actions. In order to detect the disease
supervised machine learning techniques is discussed in
this paper. With the help of Sequential ForwardSelection
(SFS) best feature will be selected for support vector
machines (SVM) model. Wisconsin breast cancerdataset
(WBCD) is used for diagnosis of breast cancer. The SVM
result shows 96% precision because of random
permutation on the data set.
Keywords :
Sequential Forward Selection SFS; Support Vector Machine; Breast Cancer; Classification; Machine Learning; Wisconsin Breast Cancer Dataset.
