The accuracy of the each model was evaluated based on the correlation coefficient, mean absolute error, mean bias error and root mean square error values.
For estimating PK measures such as AUC, Cmax, tmax, and ke, a variety of metrics including absolute average fold error (AAFE), root mean squared error (RMSE), mean ratio obs/pred), and proportion of estimates falling within a specified fold-error (i.e. 2-fold, 3-fold etc)., have been utilized.
Our model performs better than the existing models as reported by mean error, mean absolute error and mean square error.
In order to show error levels, mean absolute error and mean absolute error percentage are used.
Note: SD standard deviation; MAE mean absolute error; MSE mean square error; AE absolute error; n/a not applicable; OLS ordinary least square; CLAD censored least absolute deviation.
Also, mean square error (MSE), root of mean square error (RMSE), mean absolute error (MAE) and mean percent error (MPE) were used for evaluation of the classification.
Mean absolute error and mean absolute error percentages are very common and practical methods in literature.
Mean absolute error and mean bias error were adopted for accuracy assessment of the models.
The proposed approach achieves the state-of-the-art performance in terms of mean absolute error and mean squared error.
These include mean absolute error (MAE), root mean square error (RMSE), and relative root mean square error (RMSE%).
Mean absolute error (MAE): mean of absolute errors between estimated prediction error degrees and their true degrees.
