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One of the challenges that artificial intelligence engineers face,specifically in the field of deep learning is obtaining the optimal modelhyperparameters. The search for optimal hyperparameters usually hinders theprogress of solutions to real-world problems such as healthcare. To overcomethis hurdle, the proposed work introduces a novel mechanism called ``OptBA" toautomatically fine-tune the hyperparameters of deep learning models byleveraging the Bees Algorithm, which is a recent promising swarm intelligencealgorithm. In this paper, the optimization problem of OptBA is to maximize theaccuracy in classifying ailments using medical text, where initialhyperparameters are iteratively adjusted by specific criteria. Experimentalresults demonstrate a noteworthy enhancement in accuracy with approximately1.4%. This outcome highlights the effectiveness of the proposed mechanism inaddressing the critical issue of hyperparameter optimization and its potentialimpact on advancing solutions for healthcare and other societal challenges.

OptBA: Optimizing Hyperparameters with the Bees Algorithm for Improved Medical Text Classification