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Breast cancer is the most trending type of cancer globally with close to two and half million cases recorded based on research by the World Health Organization in 2021 and it is also the most common cancer among women in all countries, posing a major cause for public health concern. In Nigeria and the world at large, over a hundred thousand new cases of cancer occur every year with high death among women. It has been researched that early and accurate detection of breast cancer can aid in the diagnosis of the disease for women and it may also reduce the risk of death rate among women. Literature shows that several machine learning techniques have been carried out on breast cancer diagnosis to help provide accurate technology solutions to early detection. The machine learning techniques used have different accuracy rate which varies for dissimilarity conditions. In this study, we compared different methods with many existing machine-learning techniques commonly used for breast cancer detection and diagnosis. Also, the aim of this review method will show an improvement in accuracy performances by implementing different methods and analysis in existing machine learning techniques to proficiently assist doctors in decision-making on an accurate detection and diagnosis of breast cancer and classifying tumors as benign or malignant thereby reducing the risk of death rate among women.
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