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- W4285162172 abstract "One of the most common diseases among the middle-aged women is breast cancer. The proposed system designed an efficient breast cancer detection and classification approach using an ensemble booster algorithm “XGBOOST” classifier. All the traditional approaches in machine learning have developed models with low variance or with high bias. So, the proposed system has evaluated the model with different evaluation metrics. In the world of machine learning, optimization has a great impact. To address this issue, the proposed system performed whirling of hyper-parameters during the classification process. Finally, the designed system is compared with conventional models. The major goal of the proposed system is to identify the breast cancer and classify the stage of cancer. So, the automated system can help the doctors to recommend the treatment or medicines and in turn the morality rate due to the breast cancer can be reduced." @default.
- W4285162172 created "2022-07-14" @default.
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- W4285162172 date "2022-01-01" @default.
- W4285162172 modified "2023-09-27" @default.
- W4285162172 title "OXGBoost: An Optimized eXtreme Gradient Boosting Algorithm for Classification of Breast Cancer" @default.
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- W4285162172 doi "https://doi.org/10.1007/978-981-19-0840-8_4" @default.
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