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Accuracy of Diagnostic Tests

Ario Santini

Ario Santini

  • George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu MuresTargu Mures, Romania
  • Hon. Fellow, The University of EdinburghTargu Mures, Romania
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Santini, Ario
, 
Adrian Man

Adrian Man

George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu MuresTargu Mures, Romania
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Man, Adrian
 e 
Septimiu Voidăzan

Septimiu Voidăzan

George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu MuresTargu Mures, Romania
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Voidăzan, Septimiu
  
05 ago 2021
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Pubblicato online: 05 ago 2021
Pagine: 241 - 248
Ricevuto: 11 mar 2021
Accettato: 27 giu 2021
DOI: https://doi.org/10.2478/jccm-2021-0022
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© 2021 Ario Santini, Adrian Man, Septimiu Voidăzan, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
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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

True False
Predicted labels Positive TP FP
Negative FN TN
Actual labels

j_jccm-2021-0022_tab_001

Reference test (Gold standard)
Index test Positive Negative
Positive True Positive False positive
Negative False Negative True Negative

The effect of prevalence on the Positive Predictive Value

Prevalence % VVP % Sensitivity Specificity
0.1 1.8 90 95
1 15.4 90 95
5 48.6 90 95
50 94.7 90 95

Results of diagnostic tests

Reference standard
Positive Negative
Index Positive TP FP
test Negative FN TN
Lingua:
Inglese
Frequenza di pubblicazione:
4 volte all'anno
Argomenti della rivista:
Medicina, Medicina clinica, Medicina interna, Medicina interna, altro, Chirurgia, Chirurgia, altro, Anestesiologia, Medicina d'urgenza e medicina di terapia intensiva
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