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Title |
Machine Learning Techniques for Breast Cancer Analysis: A Systematic Literature Review |
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Author |
Lina Alkhathlan and Abdul Khader Jilani Saudagar |
| Citation |
Vol. 20 No. 6 pp. 83-90 |
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Abstract |
Breast cancer (BC) is one of the most common cancers and is known to be the leading cause of death among females around the world. Breast cancer occurs when cells in the breast develop a malignant tumor. Detecting BC at an early stage with state-of-the-art technologies helps in treating BC and reduces the risk of death. Currently, mammography is the most commonly used technique for detecting BC. In order to improve the mammogram analysis, researchers have studied the feasibility of using artificial intelligence to help doctors in detecting any changes that may lead to cancer. This review paper investigates utilizing machine learning (ML) algorithms for BC prediction and classification, which will be beneficial in early diagnosis and treatment for BC and for future researchers in exploring the different ML techniques and selecting the most suitable one for their future research. |
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Keywords |
Artificial neural network, Breast cancer, Machine learning, Support vector machine. |
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