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Title |
Breast Cancer Detection Using Machine Learning Algorithms |
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Author |
Abdelwadood Mesleh, Samer Hamed, Abdullah Arabiyyat |
| Citation |
Vol. 24 No. 11 pp. 141-146 |
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Abstract |
This paper presents a computer-aided design (CAD) system that detects breast cancers (BCs). BC detection uses random forest, AdaBoost, logistic regression, decision trees, na?ve Bayes and conventional neural networks (CNNs) classifiers, these machine learning (ML) based algorithms are trained to predicting BCs (malignant or benign) on BC Wisconsin data-set from the UCI repository, in which attribute clump thickness is used as evaluation class. The effectiveness of these ML algorithms are evaluated in terms of accuracy and F-measure; random forest outperformed the other classifiers and achieved 99% accuracy and 99% F-measure. |
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Keywords |
breast cancer; cancer detection, UCT; classification; machine learning |
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