Literature DB >> 25012930

Count on kappa.

Paul Czodrowski1.   

Abstract

In the 1960s, the kappa statistic was introduced for the estimation of chance agreement in inter- and intra-rater reliability studies. The kappa statistic was strongly pushed by the medical field where it could be successfully applied via analyzing diagnoses of identical patient groups. Kappa is well suited for classification tasks where ranking is not considered. The main advantage of kappa is its simplicity and the general applicability to multi-class problems which is the major difference to receiver operating characteristic area under the curve. In this manuscript, I will outline the usage of kappa for classification tasks, and I will evaluate the role and uses of kappa in specifically machine learning and cheminformatics.

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Year:  2014        PMID: 25012930     DOI: 10.1007/s10822-014-9759-6

Source DB:  PubMed          Journal:  J Comput Aided Mol Des        ISSN: 0920-654X            Impact factor:   3.686


  6 in total

1.  Bias and prevalence effects on kappa viewed in terms of sensitivity and specificity.

Authors:  F K Hoehler
Journal:  J Clin Epidemiol       Date:  2000-05       Impact factor: 6.437

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Journal:  J Chem Inf Model       Date:  2013-08-21       Impact factor: 4.956

3.  Behavior and interpretation of the kappa statistic: resolution of the two paradoxes.

Authors:  C A Lantz; E Nebenzahl
Journal:  J Clin Epidemiol       Date:  1996-04       Impact factor: 6.437

4.  High agreement but low kappa: I. The problems of two paradoxes.

Authors:  A R Feinstein; D V Cicchetti
Journal:  J Clin Epidemiol       Date:  1990       Impact factor: 6.437

5.  The measurement of observer agreement for categorical data.

Authors:  J R Landis; G G Koch
Journal:  Biometrics       Date:  1977-03       Impact factor: 2.571

6.  Bias, prevalence and kappa.

Authors:  T Byrt; J Bishop; J B Carlin
Journal:  J Clin Epidemiol       Date:  1993-05       Impact factor: 6.437

  6 in total
  9 in total

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Journal:  J Comput Aided Mol Des       Date:  2016-10-25       Impact factor: 3.686

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Journal:  J Comput Aided Mol Des       Date:  2016-03-21       Impact factor: 3.686

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9.  Diagnostic evaluation of qRT-PCR-based kit and dPCR-based kit for COVID-19.

Authors:  Cherl-Joon Lee; Wonseok Shin; Seyoung Mun; Minjae Yu; Young-Bong Choi; Dong Hee Kim; Kyudong Han
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  9 in total

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