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Published in:
2020 | OriginalPaper | Chapter
Hybrid Method for Breast Cancer Diagnosis Using Voting Technique and Three Classifiers
Authors : Hajar Saoud, Abderrahim Ghadi, Mohamed Ghailani
Published in: Innovations in Smart Cities Applications Edition 3
Publisher: Springer International Publishing
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Abstract
Breast cancer is one of the most dangerous types of cancer in women sector; it infects one woman from eight during her life and one woman from thirty die and the rate keeps increasing. The early prediction of breast cancer can make a difference and reduce the rate of mortalities, but the process of diagnosis is difficult due to the varying types of breast cancer and due to its different symptoms. So, the proposition of decision-making solution to reduce the danger of this phenomenon has become a primordial need. Machine learning techniques have proved their performance in this domain. In previous work we tested the performance of several machine learning algorithms in the classification of breast cancer such as Bayesian Networks (BN), Support Vector Machine (SVM) and k Nearest Neighbor (KNN). In this work, we will combine those classifiers using the voting technique to produce better solution using Wisconsin breast cancer dataset and WEKA tool.