Aim/PurposeThe main goal of this work is a new framework that combines genetic algorithms with deep learning. The delivery of healthcare services will be optimized as the aim of this research.

BackgroundOptimizing the provision of healthcare services is essential to ensuring that patients get suitable and timely treatments and materials.

MethodologyThis work presents a new framework for DGA-based healthcare service delivery optimization by the application of this methodology. The procedure consists of two stages: training a deep neural network to assess the feasibility of possible solutions and encoding the problem space into a format appropriate for genetic operations. The neural network evaluations are used as the guiding principle as the genetic algorithm iteratively creates a population of solutions by selection, crossover, and mutation.

ContributionThe main contribution of this work is the solution of the optimization issues related to the provision of healthcare services by combining deep learning and genetic algorithms. Ultimately, we want to improve patient outcomes and resource use by leveraging the potential of DGAs to improve the efficacy and efficiency of healthcare systems.

FindingsThe results of laboratory experiments show that the proposed approach is successful in optimizing the provision of healthcare services. The proposed DGAs enable more high-quality solutions than conventional optimization methods.

Recommendation for ResearchersThis work presents a novel framework that uses deep genetic algorithms (DGAs) to effectively optimize the provision of healthcare services and address these issues.

Future ResearchThis work can be enhanced using several deep-learning algorithms to achieve better accuracy and performance.