Mutual Information as a Performance Measure for Binary Predictors Characterized by Both ROC Curve and PROC Curve Analysis Article Swipe
G. Hughes
,
Jennifer Kopetzky
,
N. McRoberts
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3390/e22090938
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3390/e22090938
The predictive receiver operating characteristic (PROC) curve differs from the more well-known receiver operating characteristic (ROC) curve in that it provides a basis for the evaluation of binary diagnostic tests using metrics defined conditionally on the outcome of the test rather than metrics defined conditionally on the actual disease status. Application of PROC curve analysis may be hindered by the complex graphical patterns that are sometimes generated. Here we present an information theoretic analysis that allows concurrent evaluation of PROC curves and ROC curves together in a simple graphical format. The analysis is based on the observation that mutual information may be viewed both as a function of ROC curve summary statistics (sensitivity and specificity) and prevalence, and as a function of predictive values and prevalence. Mutual information calculated from a 2 × 2 prediction-realization table for a specified risk score threshold on an ROC curve is the same as the mutual information calculated at the same risk score threshold on a corresponding PROC curve. Thus, for a given value of prevalence, the risk score threshold that maximizes mutual information is the same on both the ROC curve and the corresponding PROC curve. Phytopathologists and clinicians who have previously relied solely on ROC curve summary statistics when formulating risk thresholds for application in practical agricultural or clinical decision-making contexts are thus presented with a methodology that brings predictive values within the scope of that formulation.
Related Topics
Concepts
Receiver operating characteristic
Mutual information
Mathematics
Sensitivity (control systems)
Statistics
Realization (probability)
Binary number
Function (biology)
Measure (data warehouse)
Data mining
Artificial intelligence
Computer science
Arithmetic
Engineering
Evolutionary biology
Biology
Electronic engineering
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/e22090938
- https://www.mdpi.com/1099-4300/22/9/938/pdf?version=1598839962
- OA Status
- gold
- Cited By
- 13
- References
- 24
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3080992753
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3080992753Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/e22090938Digital Object Identifier
- Title
-
Mutual Information as a Performance Measure for Binary Predictors Characterized by Both ROC Curve and PROC Curve AnalysisWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-08-26Full publication date if available
- Authors
-
G. Hughes, Jennifer Kopetzky, N. McRobertsList of authors in order
- Landing page
-
https://doi.org/10.3390/e22090938Publisher landing page
- PDF URL
-
https://www.mdpi.com/1099-4300/22/9/938/pdf?version=1598839962Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1099-4300/22/9/938/pdf?version=1598839962Direct OA link when available
- Concepts
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Receiver operating characteristic, Mutual information, Mathematics, Sensitivity (control systems), Statistics, Realization (probability), Binary number, Function (biology), Measure (data warehouse), Data mining, Artificial intelligence, Computer science, Arithmetic, Engineering, Evolutionary biology, Biology, Electronic engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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13Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4, 2024: 2, 2023: 3, 2022: 2, 2021: 1Per-year citation counts (last 5 years)
- References (count)
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24Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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