Artificial Rabbits Optimizer With Machine Learning Based Emergency Department Monitoring and Medical Data Classification at KSA Hospitals

The Emergency Departments (EDs) in health centres located the main areas of Saudi Arabia have heavy patient inflow because pandemic, viral infections, and even on some special occasions like Umrah or Hajj, where pilgrims who travel from one place to another with serious disorders. Other than EDs, it was important observe patient’s activities ED other wards region hospital track spread diseases. In this case, deep learning (DL) machine (ML) methods been used target audience classify data into many classes. With motivation, study develops an artificial rabbit optimization a learning-based healthcare classification (AROML-HDC) technique for EDs. AROML-HDC monitors tracks visit data, treatment given, length stay (LOS). addition, designs effective ARO algorithm optimal selection feature subsets. Next, class-specific cost regulation extreme (CSCR-ELM) classifier is applied medical classification. Finally, grasshopper (GOA) adjust parameters related CSCR-ELM classifier. experimental outcome approach tested benchmark Cleveland dataset Statlog comprising 297 270 samples, respectively. simulation results signify improved performance over recent maximum accuracy 93.22% 94.05% dataset,