Improving Prediction of Cervical Cancer Using KNN Imputed SMOTE Features and Multi-Model Ensemble Le...
Improving Prediction of Cervical Cancer Using KNN Imputed SMOTE Features and Multi-Model Ensemble Learning Approach
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Basel: MDPI AG
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Language
English
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Basel: MDPI AG
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Contents
Objective: Cervical cancer ranks among the top causes of death among females in developing countries. The most important procedures that should be followed to guarantee the minimizing of cervical cancer’s aftereffects are early identification and treatment under the finest medical guidance. One of the best methods to find this sort of malignancy is...
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Full title
Improving Prediction of Cervical Cancer Using KNN Imputed SMOTE Features and Multi-Model Ensemble Learning Approach
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TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_10486648
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_10486648
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ISSN
2072-6694
E-ISSN
2072-6694
DOI
10.3390/cancers15174412
