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- W4226528070 abstract "Breast cancer is one of the diseases with a high number of deaths every year and has become very prominent among Indian women in recent times. Cancer must be detected at the early stages of its formation to improve the mortality rates. This paper focuses on a comparison of multiple machine learning models with the use of different filters for the detection of breast cancer. Models used in this experiment include logistic regression, random forest, extreme gradient boosting, Gaussian Naive Bayes, K-nearest neighbors, and it is observed that the performance of these models significantly improve with the implementation of various data manipulation techniques. The best model for classification is found to be random forest (Accuracy = 98.04% and F1-Score = 96.27%) and XGBoost classifier (Accuracy = 98.32% and F1-Score = 98.31%)." @default.
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- W4226528070 date "2022-01-01" @default.
- W4226528070 modified "2023-09-28" @default.
- W4226528070 title "Comparative Study of Machine Learning Algorithms for Breast Cancer Classification" @default.
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- W4226528070 doi "https://doi.org/10.1007/978-981-16-9873-6_49" @default.
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