Comparative Analysis of Machine Learning Algorithms for Breast Cancer Prognosis

2018 ◽  
pp. 727-734 ◽  
Author(s):  
Kashish Goyal ◽  
Prakriti Sodhi ◽  
Preeti Aggarwal ◽  
Mukesh Kumar
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Comparative Analysis ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Breast Cancer Prognosis ◽  
Cancer Prognosis

scholarly journals Diagnosis of breast cancer using machine learning algorithms based on features selected by Genetic Algorithm: Assessed on five datasets

2021 ◽  
Vol 23 (11) ◽  
pp. 749-758
Author(s):  
Saranya N ◽  
◽  
Kavi Priya S ◽  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Genetic Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Cancer Prognosis ◽  
Support Vector ◽  
Breast Cancer Dataset ◽  
Human Beings ◽  
Original Dataset

Breast Cancer is one of the chronic diseases occurred to human beings throughout the world. Early detection of this disease is the most promising way to improve patients’ chances of survival. The strategy employed in this paper is to select the best features from various breast cancer datasets using a genetic algorithm and machine learning algorithm is applied to predict the outcomes. Two machine learning algorithms such as Support Vector Machines and Decision Tree are used along with Genetic Algorithm. The proposed work is experimented on five datasets such as Wisconsin Breast Cancer-Diagnosis Dataset, Wisconsin Breast Cancer-Original Dataset, Wisconsin Breast Cancer-Prognosis Dataset, ISPY1 Clinical trial Dataset, and Breast Cancer Dataset. The results exploit that SVM-GA achieves higher accuracy of 98.16% than DT-GA of 97.44%.

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