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A score S is used to predict whether a disease is present. If the score is greater than a threshold value, the corresponding person is classified as ill, otherwise as healthy. Here, the scores S, for 9 persons are given. Furthermore, it is known whether the persons are actually ill (Y, = 1) or not (Yi = 0). The data look as follows: Si | Y; 0.3 1.2 1

A score S is used to predict whether a disease is present. If the score is greater than a threshold value, the corresponding person is classified as ill, otherwise as healthy. Here, the scores S, for 9 persons are given. Furthermore, it is known whether the persons are actually ill (Y, = 1) or not (Yi = 0). The data look as follows: Si | Y; 0.3 1.2 1

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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A score S is used to predict whether a disease is present. If the score is greater than a threshold value, the corresponding person is classified as ill, otherwise as healthy. Here, the scores S; for 9 persons are given. Furthermore, it is known whether the persons are actually ill (Y, = 1) or not (Yi = 0). The data look as follows: S; | Y; 0.3 0 1.2 1 0.7 0.8 1 0.1 0.4 1 1.1 0 0.2 0 1.4 1 (a) Draw by hand the ROC curve for the given data. Explain your procedure and the calcu- lation methods. (b) Calculate the area under the curve (AUC) by hand. Does this value indicate a good or bad classification? (c) Calculate the partial area under the curve (PAUC) if the specificity is to be at least 0.65. How do you judge this value?
Transcribed Image Text:A score S is used to predict whether a disease is present. If the score is greater than a threshold value, the corresponding person is classified as ill, otherwise as healthy. Here, the scores S; for 9 persons are given. Furthermore, it is known whether the persons are actually ill (Y, = 1) or not (Yi = 0). The data look as follows: S; | Y; 0.3 0 1.2 1 0.7 0.8 1 0.1 0.4 1 1.1 0 0.2 0 1.4 1 (a) Draw by hand the ROC curve for the given data. Explain your procedure and the calcu- lation methods. (b) Calculate the area under the curve (AUC) by hand. Does this value indicate a good or bad classification? (c) Calculate the partial area under the curve (PAUC) if the specificity is to be at least 0.65. How do you judge this value?
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