文章基本信息

标题：BREAST CANCER DIAGNOSIS USING MACHINE LEARNING AND ENSEMBLE METHODS ON LARGE SEER DATABASE
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作者：HAJAR SAOUD ; ABDERRAHIM GHADI ; MOHAMED GHAILANI 等
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2021
卷号：99
期号：3
页码：594
出版社：Journal of Theoretical and Applied
摘要：Machine learning is a subdomain of artificial intelligence that has proved its performance in the medical fields, especially in the classification of the diseases. In previous researches we tried to classify breast cancer into its two categories using several machine learning algorithms, some algorithms have proved their performance but others have produced a weak accuracy. In this study, we will try to improve the accuracy of weak machine learning algorithms using the normalization/ standardization and the ensemble methods like: voting, stacking, bagging and boosting in the classification of breast cancer disease using the large SEER database and the python library. The goal of this paper is not only the improvement of the classifiers accuracy, but also the proposition of new architecture of breast cancer diagnosis based on SEER database features for predicting breast cancer in the earlier stage and with the right way. All the examined techniques have proved their performance in the improvement of the accuracy of classification of breast cancer, Specially Voting technique. It obtained the higher accuracy except the case of voting all classifiers, but it was enhanced by the normalization/ standardization of features.
关键词：SEER; Machine learning; Ensemble methods; Breast cancer; Diagnosis.