Statistical Methods - Text Mining Techniques for Healthcare Provider Quality Determination - page 123

Table 5. Misclassification rate for a 25% sample

Target Outcome Target Percentage Outcome Percentage Count Total Percentage

Training Data
0 0 80.4 96.6 10070 72.5

1 0 19.6 70.9 2462 17.7

0 1 25.6 3.3 348 2.5

1 1 74.4 29.1 1010 7.3

Validation Data
0 0 80.2 97.1 7584 72.8

1 0 19.8 71.7 1870 17.9

0 1 23.7 2.9 229 2.2

1 1 76.2 28.2 735 7.0

• The Cumulative target density is the target density computed over the irst n deciles.

• The lift for a given decile is the ratio of the target density for the decile to the target density over

all the test data.
The • Cumulative lift for a given decile is the ratio of the cumulative target density to the target

density over all the test data.

Given a lift function, we can decide on a decile cutpoint so that we can predict the high risk patients
above the cutpoint, and predict the low risk patients below a second cutpoint, while failing to make a
definite prediction for those in the center. In that way, we can dismiss those who have no risk, and ag-
gressively treat those at highest risk. Lift allows us to distinguish between patients without assuming a
uniformity of risk. Figure 15 shows the lift for the testing set when we use just the three input variables
of pneumonia, septicemia, and immune disorder.
Random chance is indicated by the lift value of 1.0; values that are higher than 1.0 indicate that the
observations are more predictable compared to random chance. In this example, 40% of the patient

Table 6. Misclassification rate for a 10% sample

Target Outcome Target Percentage Outcome Percentage Count Total Percentage

Training Data
0 0 91.5 99.3 31030 89.4

1 0 8.5 83.5 2899 8.3

0 1 27.3 0.7 216 0.6

1 1 72.6 16.5 574 1.6

Validation Data
0 0 91.5 99.2 23265 89.3

1 0 8.4 82.4 2148 8.2

0 1 27.8 0.7 176 0.7

1 1 72.2 17.5 457 1.7