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- W4367591736 abstract "Breast cancer is one of the most common types of cancer due to which death is happening at higher rate. Development of tumor is one of the symptoms of breast cancer. This paper provides a comprehensive comparative analysis of breast cancer classification concerning six machine learning algorithms: logistic regression, decision tree classifier, random forest classifier, KNN, SVM, and Keras sequential model. Wisconsin’s Breast Cancer Diagnosis dataset for this classification task is partitioned as 70% for training and 30% for testing, respectively. The features are extracted from breast mass images used for FNA tests. Parameters on which the said models are trained are selected manually. All the models mentioned give above 90% testing accuracy for this classification task." @default.
- W4367591736 created "2023-05-02" @default.
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- W4367591736 date "2023-01-01" @default.
- W4367591736 modified "2023-09-26" @default.
- W4367591736 title "Breast Cancer Classification Using ML on WDBC" @default.
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- W4367591736 doi "https://doi.org/10.1007/978-981-99-0189-0_48" @default.
- W4367591736 hasPublicationYear "2023" @default.
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