scholarly journals Improving the performance of machine learning classifiers for Breast Cancer diagnosis based on feature selection

2014 ◽  
Author(s):  
Noel Pérez ◽  
Miguel Angel Guevara ◽  
Augusto Silva ◽  
Isabel Ramos ◽  
Joana Loureiro
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Feature Selection ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Machine Learning Classifiers ◽  
Learning Classifiers

An Analysis of Machine Learning Classifiers in Breast Cancer Diagnosis

2019 ◽  
Author(s):  
Fabiano Teixeira ◽  
Joao Luis Zeni Montenegro ◽  
Cristiano Andre da Costa ◽  
Rodrigo da Rosa Righi
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Machine Learning Classifiers ◽  
Learning Classifiers

Discovering Mammography-based Machine Learning Classifiers for Breast Cancer Diagnosis

2011 ◽  
Vol 36 (4) ◽  
pp. 2259-2269 ◽  
Author(s):  
Raúl Ramos-Pollán ◽  
Miguel Angel Guevara-López ◽  
Cesar Suárez-Ortega ◽  
Guillermo Díaz-Herrero ◽  
Jose Miguel Franco-Valiente ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Machine Learning Classifiers ◽  
Learning Classifiers

scholarly journals Optimized Breast Cancer Classification using Feature Selection and Outliers Detection

2021 ◽  
pp. 298-307
Author(s):  
A. B Yusuf ◽  
R. M Dima ◽  
S. K Aina
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Feature Selection ◽  
Random Forest ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Feature Reduction ◽  
Machine Learning Algorithms ◽  
Breast Cancer Dataset ◽  
The Impact

Breast cancer is the second most commonly diagnosed cancer in women throughout the world. It is on the rise, especially in developing countries, where the majority of cases are discovered late. Breast cancer develops when cancerous tumors form on the surface of the breast cells. The absence of accurate prognostic models to assist physicians recognize symptoms early makes it difficult to develop a treatment plan that would help patients live longer. However, machine learning techniques have recently been used to improve the accuracy and speed of breast cancer diagnosis. If the accuracy is flawless, the model will be more efficient, and the solution to breast cancer diagnosis will be better. Nevertheless, the primary difficulty for systems developed to detect breast cancer using machine-learning models is attaining the greatest classification accuracy and picking the most predictive feature useful for increasing accuracy. As a result, breast cancer prognosis remains a difficulty in today's society. This research seeks to address a flaw in an existing technique that is unable to enhance classification of continuous-valued data, particularly its accuracy and the selection of optimal features for breast cancer prediction. In order to address these issues, this study examines the impact of outliers and feature reduction on the Wisconsin Diagnostic Breast Cancer Dataset, which was tested using seven different machine learning algorithms. The results show that Logistic Regression, Random Forest, and Adaboost classifiers achieved the greatest accuracy of 99.12%, on removal of outliers from the dataset. Also, this filtered dataset with feature selection, on the other hand, has the greatest accuracy of 100% and 99.12% with Random Forest and Gradient boost classifiers, respectively. When compared to other state-of-the-art approaches, the two suggested strategies outperformed the unfiltered data in terms of accuracy. The suggested architecture might be a useful tool for radiologists to reduce the number of false negatives and positives. As a result, the efficiency of breast cancer diagnosis analysis will be increased.

scholarly journals Analysis of Breast Cancer dataset using Supervised Machine Learning Classifiers

2020 ◽  
Vol 8 (6S) ◽  
pp. 161-163
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Fractal Dimension ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Supervised Machine Learning ◽  
Breast Cancer Dataset ◽  
Cancer Dataset ◽  
Machine Learning Classifiers ◽  
Supervised Machine Learning Classifiers

We Have Extracted Our Dataset From Kaggle. Our Study Is About Breast Cancer Diagnosis Based On 31 Input Attributes To Produce One Output Attribute That Is The Type Of Breast Cancer. Our Analysis Is On Two Major Aspects That Are Malignant And Benign On The Basis Of 10 Attributes That Is Texture, Perimeter, Area, Smoothness, Compactness, Concavity, Symmetry, Fractal Dimension, Concave Points And Radius.

scholarly journals Breast Cancer Diagnosis Using Machine Learning Algorithms - A Survey

2013 ◽  
Vol 4 (3) ◽  
pp. 105-112 ◽  
Author(s):  
Gayathri B.M ◽  
Sumathi C.P ◽  
Santhanam T
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Cancer Diagnosis ◽  
Learning Algorithms ◽  
Breast Cancer Diagnosis ◽  
Machine Learning Algorithms
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