Matches in SemOpenAlex for { <https://semopenalex.org/work/W4320016065> ?p ?o ?g. }
Showing items 1 to 86 of
86
with 100 items per page.
- W4320016065 endingPage "437" @default.
- W4320016065 startingPage "429" @default.
- W4320016065 abstract "Breast cancer was the most diagnosed form of cancer in 2020. Early diagnosis of breast cancer results in a significant improvement in long-term survival rates. Current methods require consultation with experts, which is expensive and time-consuming and thus may not be accessible to all. This paper seeks to train and evaluate supervised machine learning models for the accurate and efficient detection of breast cancer. The Wisconsin Breast Cancer Database dataset describes 30 attributes of cell nuclei, including, but not limited to, their radius, texture, and concavity. It contains 569 instances, 212 of which are malignant tumors. The Random Forest algorithm outperforms other algorithms in classifying breast tumors as either malignant or benign and is thus selected as our primary model. It is trained on two different subsets of the dataset having 16 and 8 features, respectively, identified with the help of multiple feature selection methods. The Random Forest models are tested post hyperparameter tuning on a holdout set, and accuracies of 100% and 99.30% respectively. The models are also compared with four other machine learning classification algorithms: Support Vector Machine (SVM), Decision Tree, Multilayer Perceptron, and K-Nearest Neighbors. The results confirm that Random Forest is the superior method for breast cancer diagnosis." @default.
- W4320016065 created "2023-02-11" @default.
- W4320016065 creator A5024337841 @default.
- W4320016065 creator A5050661587 @default.
- W4320016065 date "2023-01-01" @default.
- W4320016065 modified "2023-09-28" @default.
- W4320016065 title "Diagnosis of Breast Cancer Using Random Forests" @default.
- W4320016065 cites W1982067074 @default.
- W4320016065 cites W2012035409 @default.
- W4320016065 cites W2020089616 @default.
- W4320016065 cites W2056137745 @default.
- W4320016065 cites W2103970959 @default.
- W4320016065 cites W2161741939 @default.
- W4320016065 cites W2727347885 @default.
- W4320016065 cites W2761181345 @default.
- W4320016065 cites W2806359329 @default.
- W4320016065 cites W2990979467 @default.
- W4320016065 cites W3014679091 @default.
- W4320016065 cites W3015582604 @default.
- W4320016065 cites W3021329907 @default.
- W4320016065 cites W3095666019 @default.
- W4320016065 cites W3106717751 @default.
- W4320016065 cites W3124051732 @default.
- W4320016065 cites W3128646645 @default.
- W4320016065 cites W3197078391 @default.
- W4320016065 cites W92469554 @default.
- W4320016065 doi "https://doi.org/10.1016/j.procs.2023.01.025" @default.
- W4320016065 hasPublicationYear "2023" @default.
- W4320016065 type Work @default.
- W4320016065 citedByCount "3" @default.
- W4320016065 countsByYear W43200160652023 @default.
- W4320016065 crossrefType "journal-article" @default.
- W4320016065 hasAuthorship W4320016065A5024337841 @default.
- W4320016065 hasAuthorship W4320016065A5050661587 @default.
- W4320016065 hasBestOaLocation W43200160651 @default.
- W4320016065 hasConcept C119857082 @default.
- W4320016065 hasConcept C121608353 @default.
- W4320016065 hasConcept C12267149 @default.
- W4320016065 hasConcept C126322002 @default.
- W4320016065 hasConcept C148483581 @default.
- W4320016065 hasConcept C153180895 @default.
- W4320016065 hasConcept C154945302 @default.
- W4320016065 hasConcept C169258074 @default.
- W4320016065 hasConcept C179717631 @default.
- W4320016065 hasConcept C41008148 @default.
- W4320016065 hasConcept C50644808 @default.
- W4320016065 hasConcept C530470458 @default.
- W4320016065 hasConcept C60908668 @default.
- W4320016065 hasConcept C71924100 @default.
- W4320016065 hasConcept C84525736 @default.
- W4320016065 hasConcept C8642999 @default.
- W4320016065 hasConceptScore W4320016065C119857082 @default.
- W4320016065 hasConceptScore W4320016065C121608353 @default.
- W4320016065 hasConceptScore W4320016065C12267149 @default.
- W4320016065 hasConceptScore W4320016065C126322002 @default.
- W4320016065 hasConceptScore W4320016065C148483581 @default.
- W4320016065 hasConceptScore W4320016065C153180895 @default.
- W4320016065 hasConceptScore W4320016065C154945302 @default.
- W4320016065 hasConceptScore W4320016065C169258074 @default.
- W4320016065 hasConceptScore W4320016065C179717631 @default.
- W4320016065 hasConceptScore W4320016065C41008148 @default.
- W4320016065 hasConceptScore W4320016065C50644808 @default.
- W4320016065 hasConceptScore W4320016065C530470458 @default.
- W4320016065 hasConceptScore W4320016065C60908668 @default.
- W4320016065 hasConceptScore W4320016065C71924100 @default.
- W4320016065 hasConceptScore W4320016065C84525736 @default.
- W4320016065 hasConceptScore W4320016065C8642999 @default.
- W4320016065 hasLocation W43200160651 @default.
- W4320016065 hasOpenAccess W4320016065 @default.
- W4320016065 hasPrimaryLocation W43200160651 @default.
- W4320016065 hasRelatedWork W3028499805 @default.
- W4320016065 hasRelatedWork W3034132578 @default.
- W4320016065 hasRelatedWork W3150651898 @default.
- W4320016065 hasRelatedWork W3168994312 @default.
- W4320016065 hasRelatedWork W3202148033 @default.
- W4320016065 hasRelatedWork W4231578810 @default.
- W4320016065 hasRelatedWork W4282977429 @default.
- W4320016065 hasRelatedWork W4310436134 @default.
- W4320016065 hasRelatedWork W4321636153 @default.
- W4320016065 hasRelatedWork W4362564095 @default.
- W4320016065 hasVolume "218" @default.
- W4320016065 isParatext "false" @default.
- W4320016065 isRetracted "false" @default.
- W4320016065 workType "article" @default.