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- W4285157046 abstract "The most vicious malady among women is breast cancer. The expeditious detection will aid to escalate the survival rate and also dwindle the mortality rate due to breast cancer. A machine learning-based system is proposed for the explicit diagnosis of breast cancer. Support vector machine (SVM), K-nearest neighbour (K-NN), Naive Bayes (NB), decision tree (DT) and random forest (RF) are being used for the development of the model. The significant features for the model are selected by using least absolute shrinkage and selection operator (Lasso). Accuracy, misclassification, sensitivity, specificity, F1 score, Mathew’s correlation coefficient (MCC) and Youden’s index (J) were calculated for model diagnosis assessment." @default.
- W4285157046 created "2022-07-14" @default.
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- W4285157046 date "2022-01-01" @default.
- W4285157046 modified "2023-09-30" @default.
- W4285157046 title "Feature Selection and Diagnosis Performance Evaluation of Breast Cancer" @default.
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- W4285157046 doi "https://doi.org/10.1007/978-981-19-1111-8_44" @default.
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