**5. Conclusions**

Finally, we analyzed if there was the difference, between features selected on the first day, where the predictive performance was consistently poor, and other days, where the predictive performance was acceptable. Rank means and standard deviations were calculated for two groups: all days (with day one) and days 2–7 (without day 1) (**Figure 3**). Standard deviations of ranks are much higher over ranks of features that include day 1 (right parts of **Figure 3**). The average ranks changed (middle part of the figure), but similar laboratory tests were in first 15 ranks in both cases.

**Figure 3.** Pyramid chart of feature ranking (laboratory tests) for predictive value by mean and standard deviation; in-

This study using ensemble methods demonstrated an improvement in predictive accuracy compared to prediction based on single models. Random Forests seem to provide the best predictive accuracy complying with our previous research [8]. (**Table 3**) Random Forest also

**4. Discussion**

and -excluding day 1.

102 Data Mining

Predictive analytics using ensemble methods are able to predict hospital or ICU outcome of renal patients with high accuracy. Predictive accuracy changes with the length of stay. Feature ranking enables quantitative assessment of patient data (e.g. laboratory tests) for predictive power. Lactate and white blood cell count best predict hospital mortality in this population. From the second day of ICU admission, predictive accuracy based on laboratory tests >80%. This generates opportunities for efficacy and efficiency analysis of other data recorded during ICU stay.
