Breast Cancer Classification Using Machine Learning Algorithms

2020 ◽  
pp. 571-578
Author(s):  
Simran Sharma ◽  
Sachin Deshpande
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Learning Algorithms ◽  
Cancer Classification ◽  
Machine Learning Algorithms ◽  
Breast Cancer Classification

scholarly journals Breast cancer classification using machine learning techniques: a comparative study

2020 ◽  
Vol 4 (2) ◽  
pp. 535-544
Author(s):  
Djihane HOUFANI ◽  
◽  
Sihem SLATNIA ◽  
Okba KAZAR ◽  
Noureddine ZERHOUNI ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Logistic Regression ◽  
Multilayer Perceptron ◽  
Learning Algorithms ◽  
Cancer Classification ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Breast Cancer Classification ◽  
Learning Techniques

Background: The second leading deadliest disease affecting women worldwide, after lung cancer, is breast cancer. Traditional approaches for breast cancer diagnosis suffer from time consumption and some human errors in classification. To deal with this problems, many research works based on machine learning techniques are proposed. These approaches show their effectiveness in data classification in many fields, especially in healthcare. Methods: In this cross sectional study, we conducted a practical comparison between the most used machine learning algorithms in the literature. We applied kernel and linear support vector machines, random forest, decision tree, multi-layer perceptron, logistic regression, and k-nearest neighbors for breast cancer tumors classification. The used dataset is Wisconsin diagnosis Breast Cancer. Results: After comparing the machine learning algorithms efficiency, we noticed that multilayer perceptron and logistic regression gave the best results with an accuracy of 98% for breast cancer classification. Conclusion: Machine learning approaches are extensively used in medical prediction and decision support systems. This study showed that multilayer perceptron and logistic regression algorithms are performant ( good accuracy specificity and sensitivity) compared to the other evaluated algorithms.

THE EFFECT OF DATASETS ON BREAST CANCER DETECTION MODELS

2021 ◽  
Vol 4 (4) ◽  
pp. 309-315
Author(s):  
Kumawuese Jennifer Kurugh ◽  
Muhammad Aminu Ahmad ◽  
Awwal Ahmad Babajo
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Learning Algorithms ◽  
Cancer Classification ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Breast Cancer Dataset ◽  
Machine Learning Classifiers ◽  
Breast Cancer Classification ◽  
Learning Classifiers

Datasets are a major requirement in the development of breast cancer classification/detection models using machine learning algorithms. These models can provide an effective, accurate and less expensive diagnosis method and reduce life losses. However, using the same machine learning algorithms on different datasets yields different results. This research developed several machine learning models for breast cancer classification/detection using Random forest, support vector machine, K Nearest Neighbors, Gaussian Naïve Bayes, Perceptron and Logistic regression. Three widely used test data sets were used; Wisconsin Breast Cancer (WBC) Original, Wisconsin Diagnostic Breast Cancer (WDBC) and Wisconsin Prognostic Breast Cancer (WPBC). The results show that datasets affect the performance of machine learning classifiers. Also, the machine learning classifiers have different performances with a given breast cancer dataset

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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