To ascertain the model performance, the model needs to run on at least one year (most often, two years) of historical data, requiring advanced computer power.
To determine model performance, the root mean square error (RMSE) was used.
To examine model performance, the "possible diabetes" group was combined with the "no diabetes" group to allow for a dichotomous classification.
To tests model performance, the southern localities were partitioned into two halves with random assignment: a training and a testing set.
Generally, the larger the PCC value, the better the model performance, while the lower the RMSE/MAE/MAPE value, the better the model performance.
Figure 3 Model performances on the different feature sets.
To further evaluate the models' performance the echocardiography data obtained within ±3 month from the index hospitalization for 854 subjects admitted to one of the hospital were assessed.
For ICC values of 15% or 30%, the model performance of the standard model and the marginal risk calculation was better compared to model performance in datasets without associations between the clustering and a predictor.
Finally, we compared these results with the original model performances applied to the whole chemical space.
The measures of the model performance of the model h2 are only slightly worse than those of h4.
