In the project "Röstanalys och ansiktsigenkänning hos akromegalipatienter", several acoustic properties (>7000) have been extracted from the speech signal for each of 258 speakers that either have the disease or is a control. We have developed an ensemble model that may relatively accurately detect which speaker is a person with acromegaly, and who is not. However, the model tuning of each component model is prohibitively slow (~60 hours on an M1 computer with 10 CPU cores), and we need separate compute time to perform an estimation of how robust the findings are to different test/validation set splits.