Statistical Methods - Text Mining Techniques for Healthcare Provider Quality Determination - page 122

Figure 11. ROC curve for traditional logistic regression

Figure 12. ROC from predictive modeling

IntroductIon to lIft

Lift allows us to find the patients at highest risk for occurrence, and with the greatest probability of
accurate prediction. This is especially important since these are the patients we would want to take the
greatest care for, and who will incur the highest costs and longest length of stay.
Using lift, true positive patients with highest confidence come first, followed by positive patients
with lower confidence. True negative cases with lowest confidence come next, followed by negative
cases with highest confidence. Based on that ordering, the observations are partitioned into deciles, and
the following statistics are calculated:

• The Target density of a decile is the number of actually positive instances in that decile divided by

the total number of instances in the decile.