A Data-Driven Predictive Approach for Drug Delivery Using Machine Learning Techniques
Figure 7
The ROC curve for determining prediction performance.
The ROC curve shows the tradeoff between sensitivity and specificity (any increase in sensitivity will be accompanied by a decrease in specificity). The closer the curve is to the minimum false alarm rate (x-axis) and the maximum sensitivity (y-axis), the more accurate the test. As the ROC curve approaches y = x, the less accurate the test becomes. The intersection point of the ROC curve with the line y = −x is defined as the optimum operation point. In this ROC curve, the optimum operation point had an 80% true positive rate, with a 20% false positive rate.