ROC Solid: Receiver Operator Characteristic (ROC) Curves as a Foundation for Better Diagnostic Tests

An ROC curve describes the relationship between the sensitivity and specificity of a test by plotting the two against one another while varying the CV. It is helpful when the outcome of a diagnostic test is continuous or ordinal. The key to an effective diagnostic test is to accurately classify 2 distinct populations into their respective groups of diseased versus nondiseased. Choosing the optimum CV is a tradeoff between the sensitivity (true positive rate) and the false positive rate. ROC curves are an important tool in evaluating the shape of uncertainty and are a valuable method in characterizing the strengths and weaknesses of diagnostic tests. The AUC provides a single quantitative, index measure for assessing the performance of a diagnostic test. They also provide an intuitive, qualitative assessment of the interrelated dynamics influencing decisions behind diagnostic tests. Their effectiveness spans multiple fields of study and their presence in modern diagnostic analysis is likely only to grow.