  Prediction of poly(lactic co glycolic acid) (PLGA) micro- and nanoparticles'
dissolution rates plays a significant role in pharmaceutical and medical
industries. The prediction of PLGA dissolution rate is crucial for drug
manufacturing. Therefore, a model that predicts the PLGA dissolution rate could
be beneficial. PLGA dissolution is influenced by numerous factors (features),
and counting the known features leads to a dataset with 300 features. This
large number of features and high redundancy within the dataset makes the
prediction task very difficult and inaccurate. In this study, dimensionality
reduction techniques were applied in order to simplify the task and eliminate
irrelevant and redundant features. A heterogeneous pool of several regression
algorithms were independently tested and evaluated. In addition, several
ensemble methods were tested in order to improve the accuracy of prediction.
The empirical results revealed that the proposed evolutionary weighted ensemble
method offered the lowest margin of error and significantly outperformed the
individual algorithms and the other ensemble techniques.
