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Accuracy of Diagnostic Tests Cover

Accuracy of Diagnostic Tests

The Journal of Critical Care Medicine
Volume 7 (2021): Issue 3 (July 2021)
By: Ario Santini,  Adrian Man and  Septimiu Voidăzan  
Open Access
|Aug 2021

Figures & Tables

Fig. 1

The ROC curve is composed by calculating the Sensitivity and the False Positive Rate for several thresholds, and plotting them against each other. The False Positive Rate (FPR) or 1 – Specificity is a measurement of how accurate the real negatives are being recorded. The smaller the FPR, the more accurate the identification of the real negative in the data. Sensitivity is recorded on the y axis and is a measure of how accurate people who have a disease are being identified as such.
The ROC curve is composed by calculating the Sensitivity and the False Positive Rate for several thresholds, and plotting them against each other. The False Positive Rate (FPR) or 1 – Specificity is a measurement of how accurate the real negatives are being recorded. The smaller the FPR, the more accurate the identification of the real negative in the data. Sensitivity is recorded on the y axis and is a measure of how accurate people who have a disease are being identified as such.

Fig. 2

Representation of the ROC for a random model
Representation of the ROC for a random model

Fig. 3

Explanation of different points of an ROC curve
Explanation of different points of an ROC curve

Fig. 4

The area under the ROC curve (AUC) is a measurement from values of 0.5 (random classifier) to 1 (perfect classifier). It signifies how well the model classifies the True and False data points. The greater AUC results in the ROC approaching the desired top-left corner.
The area under the ROC curve (AUC) is a measurement from values of 0.5 (random classifier) to 1 (perfect classifier). It signifies how well the model classifies the True and False data points. The greater AUC results in the ROC approaching the desired top-left corner.

Fig. 5

An example for ROC curves of age and ESR in cancer. For age the area under the curve is 0.684, and for ESR = 0.690. It can be seen how the curves are closer to the reference line (area = 0.5) than to the upper left corner, the point of maximum accuracy of the test.
An example for ROC curves of age and ESR in cancer. For age the area under the curve is 0.684, and for ESR = 0.690. It can be seen how the curves are closer to the reference line (area = 0.5) than to the upper left corner, the point of maximum accuracy of the test.

j_jccm-2021-0022_tab_004

TrueFalse
Predicted labelsPositiveTPFP
NegativeFNTN
Actual labels

j_jccm-2021-0022_tab_001

Reference test (Gold standard)
Index testPositiveNegative
PositiveTrue PositiveFalse positive
NegativeFalse NegativeTrue Negative

The effect of prevalence on the Positive Predictive Value

Prevalence %VVP %SensitivitySpecificity
0.11.89095
115.49095
548.69095
5094.79095

Results of diagnostic tests

Reference standard
PositiveNegative
IndexPositiveTPFP
testNegativeFNTN
DOI: https://doi.org/10.2478/jccm-2021-0022 | Journal eISSN: 2393-1817 | Journal ISSN: 2393-1809
Journal RSS Feed
Language: English
Page range: 241 - 248
Submitted on: Mar 11, 2021
|
Accepted on: Jun 27, 2021
|
Published on: Aug 5, 2021
Published by: University of Medicine, Pharmacy, Science and Technology of Targu Mures
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
2019 (COVID-19) pandemic,
diagnostic tests,
ROC curve,
Receiver Operating Characteristic
Related subjects:
Medicine,
Clinical medicine,
Internal medicine,
Internal medicine, other,
Surgery,
Surgery, other,
Anaesthesiology,
Emergency medicine and intensive-care medicine

© 2021 Ario Santini, Adrian Man, Septimiu Voidăzan, published by University of Medicine, Pharmacy, Science and Technology of Targu Mures
This work is licensed under the Creative Commons Attribution 4.0 License.

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