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Lastly, we applied supervised classification with Random Forests.

A random forest provided with a fused feature set, comprising the best representations of clinical measurements and clinical codes, was then built and compared with random forests with access only to one of the data types.

The prediction performance is still comparable with random forests in terms of performance within small amount of Gaussian noise.

In general, the models developed with random forest and kappa nearest neighbor were reasonably good in predicting the test set (accuracy 67 70%), with random forest performing slightly better (MCC 0.41 vs 0.34 for kappa nearest neighbor; G-mean (0.66/0.70).

However, no study has yet been done on performance-driven model building with random forest (RF -based variable selection.

First, the DP model captures the shape and temporal structure which is not the case with random forest.

Other predictor variables were identified with Random Forest.

Our approach has similar values of R2 compared with those of random forests.

The relationships between structure and biological activity in these large pooled datasets are non-linear and best modeled using non-linear methods such as Random Forests (RF) or Support Vector Machines (SVM) with non-linear kernels.

We compared different implementations of random forests with different split criteria and found that bivariate node-splitting random survival forests with log-rank test is among the best.

A predictor panel of 15 variables enabled an optimal classification by Random Forests with an error rate of 24±1%.