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Breast cancer is the most common type of cancer in women. When cancers are found early, they can often be cured. There are some devices that detect the breast cancer but many times they lead to false positives, which results in patients undergoing painful, expensive surgeries that were not even necessary. These type of cancers are called benign which do not require surgeries and we can reduce these unnecessary surgeries by using Machine Learning. We take a dataset of the previous breast cancer patients and train the model to predict whether the cancer is benign or malignant. The objective of these predictions is to assign patients to either a benign group that is noncancerous or a malignant group that is cancerous. These predictions will help doctors to do surgeries only when the cancer is malignant, thus reducing the unnecessary surgeries for woman.
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Breast Cancer Prediction Using Machine Learning Algorithms
Verlag:
LAP LAMBERT Academic Publishing Jul 2023, 2023
ISBN 10: 6206686523
ISBN 13: 9786206686521
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Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
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Taschenbuch. Zustand: Neu. Neuware -Breast cancer is the most common type of cancer in women. When cancers are found early, they can often be cured. There are some devices that detect the breast cancer but many times they lead to false positives, which results in patients undergoing painful, expensive surgeries that were not even necessary. These type of cancers are called benign which do not require surgeries and we can reduce these unnecessary surgeries by using Machine Learning. We take a dataset of the previous breast cancer patients and train the model to predict whether the cancer is benign or malignant. The objective of these predictions is to assign patients to either a benign group that is noncancerous or a malignant group that is cancerous. These predictions will help doctors to do surgeries only when the cancer is malignant, thus reducing the unnecessary surgeries for woman.Books on Demand GmbH, Überseering 33, 22297 Hamburg 68 pp. Englisch. Artikel-Nr. 9786206686521
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