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LDA–GA–SVM: improved hepatocellular carcinoma prediction through dimensionality reduction and geneti...

LDA–GA–SVM: improved hepatocellular carcinoma prediction through dimensionality reduction and geneti...

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2502555733

LDA–GA–SVM: improved hepatocellular carcinoma prediction through dimensionality reduction and genetically optimized support vector machine

About this item

Full title

LDA–GA–SVM: improved hepatocellular carcinoma prediction through dimensionality reduction and genetically optimized support vector machine

Publisher

London: Springer London

Journal title

Neural computing & applications, 2021-04, Vol.33 (7), p.2783-2792

Language

English

Formats

Publication information

Publisher

London: Springer London

More information

Scope and Contents

Contents

Hepatocellular carcinoma (HCC) is a common type of liver cancer worldwide. Patients with HCC have rare chances of survival. The chances of survival increase, if the cancer is diagnosed early. Hence, different machine learning-based methods have been developed by researchers for the accurate detection of HCC. However, high dimensionality (curse of d...

Alternative Titles

Full title

LDA–GA–SVM: improved hepatocellular carcinoma prediction through dimensionality reduction and genetically optimized support vector machine

Identifiers

Primary Identifiers

Record Identifier

TN_cdi_proquest_journals_2502555733

Permalink

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2502555733

Other Identifiers

ISSN

0941-0643

E-ISSN

1433-3058

DOI

10.1007/s00521-020-05157-2

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