  One of the main challenges in the field of deep learning is obtaining the
optimal model hyperparameters. The search for optimal hyperparameters usually
hinders the progress of solutions to real-world problems such as healthcare.
Previous solutions have been proposed, but they can still get stuck in local
optima. To overcome this hurdle, we propose OptBA to automatically fine-tune
the hyperparameters of deep learning models by leveraging the Bees Algorithm,
which is a recent promising swarm intelligence algorithm. In this paper, the
optimization problem of OptBA is to maximize the accuracy in classifying
ailments using medical text, where initial hyperparameters are iteratively
adjusted by specific criteria. Experimental results demonstrate a noteworthy
enhancement in accuracy with approximately 1.4%. This outcome highlights the
effectiveness of the proposed mechanism in addressing the critical issue of
hyperparameter optimization and its potential impact on advancing solutions for
healthcare. The code is available publicly at
\url{https://github.com/Mai-CS/OptBA}.
