FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2133647570> ?p ?o ?g. }

• W2133647570 endingPage "204" @default.
• W2133647570 startingPage "183" @default.
• W2133647570 abstract "There are a number of different quantitative models that can be used in a medical diagnostic decision support system (MDSS) including parametric methods (linear discriminant analysis or logistic regression), non-parametric models (K nearest neighbor, or kernel density) and several neural network models. The complexity of the diagnostic task is thought to be one of the prime determinants of model selection. Unfortunately, there is no theory available to guide model selection. Practitioners are left to either choose a favorite model or to test a small subset using cross validation methods. This paper illustrates the use of a self-organizing map (SOM) to guide model selection for a breast cancer MDSS. The topological ordering properties of the SOM are used to define targets for an ideal accuracy level similar to a Bayes optimal level. These targets can then be used in model selection, variable reduction, parameter determination, and to assess the adequacy of the clinical measurement system. These ideas are applied to a successful model selection for a real-world breast cancer database. Diagnostic accuracy results are reported for individual models, for ensembles of neural networks, and for stacked predictors." @default.
• W2133647570 created "2016-06-24" @default.
• W2133647570 creator A5050019388 @default.
• W2133647570 creator A5087920867 @default.
• W2133647570 date "2000-11-01" @default.
• W2133647570 modified "2023-09-23" @default.
• W2133647570 title "Model selection for a medical diagnostic decision support system: a breast cancer detection case" @default.
• W2133647570 cites W1867138509 @default.
• W2133647570 cites W1966829527 @default.
• W2133647570 cites W1968968976 @default.
• W2133647570 cites W1972374016 @default.
• W2133647570 cites W1974788427 @default.
• W2133647570 cites W1981103483 @default.
• W2133647570 cites W1984419199 @default.
• W2133647570 cites W1984889947 @default.
• W2133647570 cites W1985009090 @default.
• W2133647570 cites W1985265521 @default.
• W2133647570 cites W1989164753 @default.
• W2133647570 cites W1991513497 @default.
• W2133647570 cites W1991859953 @default.
• W2133647570 cites W1997777628 @default.
• W2133647570 cites W1998447990 @default.
• W2133647570 cites W1998612546 @default.
• W2133647570 cites W1998878162 @default.
• W2133647570 cites W2003046741 @default.
• W2133647570 cites W2004471885 @default.
• W2133647570 cites W2011433920 @default.
• W2133647570 cites W2014374971 @default.
• W2133647570 cites W2014922235 @default.
• W2133647570 cites W2017713426 @default.
• W2133647570 cites W2025182061 @default.
• W2133647570 cites W2032882144 @default.
• W2133647570 cites W2035602263 @default.
• W2133647570 cites W2035810868 @default.
• W2133647570 cites W2037976962 @default.
• W2133647570 cites W2043868637 @default.
• W2133647570 cites W2046575449 @default.
• W2133647570 cites W2056863478 @default.
• W2133647570 cites W2062884568 @default.
• W2133647570 cites W2064582139 @default.
• W2133647570 cites W2068357300 @default.
• W2133647570 cites W2074599392 @default.
• W2133647570 cites W2077619161 @default.
• W2133647570 cites W2078322828 @default.
• W2133647570 cites W2108727998 @default.
• W2133647570 cites W2124152802 @default.
• W2133647570 cites W2139372732 @default.
• W2133647570 cites W2140218006 @default.
• W2133647570 cites W2148965132 @default.
• W2133647570 cites W2149723649 @default.
• W2133647570 cites W2150884987 @default.
• W2133647570 cites W2151817208 @default.
• W2133647570 cites W2153379234 @default.
• W2133647570 cites W2164160681 @default.
• W2133647570 cites W2169472609 @default.
• W2133647570 cites W2180456856 @default.
• W2133647570 cites W2400674531 @default.
• W2133647570 cites W2796765386 @default.
• W2133647570 cites W28412257 @default.
• W2133647570 cites W4212883601 @default.
• W2133647570 cites W4237222446 @default.
• W2133647570 cites W4240294902 @default.
• W2133647570 doi "https://doi.org/10.1016/s0933-3657(00)00063-4" @default.
• W2133647570 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/10998586" @default.
• W2133647570 hasPublicationYear "2000" @default.
• W2133647570 type Work @default.
• W2133647570 sameAs 2133647570 @default.
• W2133647570 citedByCount "86" @default.
• W2133647570 countsByYear W21336475702012 @default.
• W2133647570 countsByYear W21336475702013 @default.
• W2133647570 countsByYear W21336475702014 @default.
• W2133647570 countsByYear W21336475702015 @default.
• W2133647570 countsByYear W21336475702017 @default.
• W2133647570 countsByYear W21336475702018 @default.
• W2133647570 countsByYear W21336475702019 @default.
• W2133647570 countsByYear W21336475702020 @default.
• W2133647570 countsByYear W21336475702021 @default.
• W2133647570 countsByYear W21336475702022 @default.
• W2133647570 countsByYear W21336475702023 @default.
• W2133647570 crossrefType "journal-article" @default.
• W2133647570 hasAuthorship W2133647570A5050019388 @default.
• W2133647570 hasAuthorship W2133647570A5087920867 @default.
• W2133647570 hasConcept C105795698 @default.
• W2133647570 hasConcept C107673813 @default.
• W2133647570 hasConcept C117251300 @default.
• W2133647570 hasConcept C119857082 @default.
• W2133647570 hasConcept C124101348 @default.
• W2133647570 hasConcept C148483581 @default.
• W2133647570 hasConcept C151956035 @default.
• W2133647570 hasConcept C154945302 @default.
• W2133647570 hasConcept C207201462 @default.
• W2133647570 hasConcept C33923547 @default.
• W2133647570 hasConcept C41008148 @default.
• W2133647570 hasConcept C50644808 @default.
• W2133647570 hasConcept C69738355 @default.
• W2133647570 hasConcept C81917197 @default.
• W2133647570 hasConcept C93959086 @default.
• W2133647570 hasConceptScore W2133647570C105795698 @default.

• next