ASSIGNMENT.pptx. explaining about Rock curve
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The document discusses the receiver operator characteristic (ROC) curve, which is a graphical representation of the performance of a binary classification model across different thresholds. It plots the true positive rate against the false positive rate. An accurate test will have a curve closer to the upper left corner. The ROC curve can be used to determine an optimal threshold, assess diagnostic test accuracy, compare multiple tests, and calculate the area under the curve (AUC) as a measure of diagnostic efficacy. It has applications in evaluating the sensitivity and specificity of different diagnostic tests.
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ASSIGNMENT.pptx. explaining about Rock curve
- 1. DEPARTMENT OF MICROBIOLOGY AND IMMUNOLOGY ASSIGNMENT Coordinator : Dr. Mtebe Majigo Presenter: MSc MI 2nd year Date: 09/01/2024
- 2. Receiver operator characteristic (ROC) CURVE • ROC curve is a graphical representation of the performance of a binary classification model for all classification thresholds • It is a plot of the "true-positives" against the "false- positives." • As the curve approaches the upper left hand corner of the graph, the test becomes more accurate. • The diagonal line represents the chance that a test will be positive or negative by chance. • .
- 4. • It used to determining cut off which should optimize the sensitivity and specificity of diagnostic tests • It help to asses the accuracy of a diagnostic test. • Can be used to compare couple or more diagnostic test • The statistical measure yielded by the ROC curve that tests the diagnostic efficacy of a test is called the area under the curve (AUC) or c-statistic. • Receiver operator characteristic curves can also be used to test the sensitivity and specificity of different diagnostic tests against each other. APPLICATION
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- #6: Lymphocytes, neutrophils and CRP were used to predict the possible causative agents. Children with viral pathogens had significantly elevated lymphocytes, with normal or elevated CRP. Te sensitivity of elevated lymphocytes in detecting viral pathogens was 73.4%