BEE COLONY OPTIMISATION FOR HEALTHCARE SERVICE OPTIMIZATION: A CASE STUDY IN HOSPITAL SCHEDULING

Abstract

Aim: Some of the adverse health impacts that have been connected to air pollution include premature mortality, cardiovascular disease, bronchitis, asthma, and cancer.

Background: Air pollution has a detrimental effect on both the health and well-being of an individual, it raises significant concerns with regard to public health. Due to the expanding number of people living in these sorts of cities and the increased usage of fossil fuels, the infrastructure of these kinds of cities is experiencing an increasing amount of strain.

Contribution:  Within the scope of this study, we examine the classification of lung scans obtained from paediatric respiratory patients by making use of healthcare management scheduling in order to identify possible instances of sickness.

Methodology: First, the images are subjected to pre-processing, which is accomplished by the application of a modified bee colony optimisation technique.

Findings:The architecture of the model enables it to perform more accurately than other methods compared to its existing methods.

Recommendation for Researchers: Furthermore, in terms of computing efficiency and accuracy of prediction, our method beat those of previous methods.

Future Research: In future research, this work can be enhanced using several deep learning algorithms for achieving better accuracy and performance.