In a Receiver Operating Characteristic (ROC) curve, what is plotted on the X-axis?

In a Receiver Operating Characteristic (ROC) curve, what is plotted on the X-axis?

• A. True positives
• B. False positives
• C. True negatives
• D. False negatives

Answer: (B)

In a Receiver Operating Characteristic (ROC) curve, the X-axis represents the false positive rate, which is actually measured as the proportion of negative cases that are incorrectly classified as positive. This is calculated using the formula false positive rate = false positives / (false positives + true negatives). The ROC curve illustrates the trade-off between sensitivity (true positive rate) and the false positive rate across different threshold settings. By plotting the false positive rate on the X-axis against the true positive rate on the Y-axis, one can visualize how well a diagnostic test can discriminate between the positive and negative classes as the threshold for categorization changes. This framework is crucial for understanding the performance of diagnostic tests in a variety of fields, including medicine, where the ability to minimize false positives while correctly identifying true positives is essential for effective patient care.

In a Receiver Operating Characteristic (ROC) curve, the X-axis represents the false positive rate, which is actually measured as the proportion of negative cases that are incorrectly classified as positive. This is calculated using the formula false positive rate = false positives / (false positives + true negatives).

The ROC curve illustrates the trade-off between sensitivity (true positive rate) and the false positive rate across different threshold settings. By plotting the false positive rate on the X-axis against the true positive rate on the Y-axis, one can visualize how well a diagnostic test can discriminate between the positive and negative classes as the threshold for categorization changes.

This framework is crucial for understanding the performance of diagnostic tests in a variety of fields, including medicine, where the ability to minimize false positives while correctly identifying true positives is essential for effective patient care.