Comparison of Different Classification Techniques Using WEKA for Breast Cancer

2007 ◽  
pp. 520-523 ◽  
Author(s):  
Mohd Fauzi bin Othman ◽  
Thomas Moh Shan Yau
Keyword(s):  
Breast Cancer ◽  
Classification Techniques

scholarly journals Prediction and Classification into Benign and Malignant using the Clinical Testing Features

2020 ◽  
Vol 9 (10) ◽  
pp. 55-61
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Image Classification ◽  
Naive Bayes ◽  
Malignant Tumors ◽  
Naïve Bayes ◽  
Support Vector ◽  
Natural Image ◽  
Data Set ◽  
Classification Techniques

Breast Cancer is the most often identified cancer among women and a major reason for the increased mortality rate among women. As the diagnosis of this disease manually takes long hours and the lesser availability of systems, there is a need to develop the automatic diagnosis system for early detection of cancer. The advanced engineering of natural image classification techniques and Artificial Intelligence methods has largely been used for the breast-image classification task. Data mining techniques contribute a lot to the development of such a system, Classification, and data mining methods are an effective way to classify data. For the classification of benign and malignant tumors, we have used classification techniques of machine learning in which the machine learns from the past data and can predict the category of new input. This study is a relative study on the implementation of models using Support Vector Machine (SVM), and Naïve Bayes on Breast cancer Wisconsin (Original) Data Set. With respect to the results of accuracy, precision, sensitivity, specificity, error rate, and f1 score, the efficiency of each algorithm is measured and compared. Our experiments have shown that SVM is the best for predictive analysis with an accuracy of 99.28% and naïve Bayes with an accuracy of 98.56%. It is inferred from this study that SVM is the well-suited algorithm for prediction.

scholarly journals An Application of Classification Techniques on Breast Cancer Prognosis

2012 ◽  
Vol 59 (3) ◽  
pp. 6-10 ◽  
Author(s):  
Sandeep Chaurasia ◽  
Prasun Chakrabarti ◽  
Neha Chourasia
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Prognosis ◽  
Cancer Prognosis ◽  
Classification Techniques

Analysis and prediction of breast cancer through feature selection and classification techniques

2021 ◽  
Vol 13 (5) ◽  
pp. 359
Author(s):  
P. Balasubramanian ◽  
J. Sanjivi ◽  
A. Sathish Kumar ◽  
E. Sivasankar
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Classification Techniques
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