Artificial Intelligence Enabled Decision Support System on E-Healthcare Environment

In today’s digital era, e-healthcare systems exploit technologies and telecommunication devices such as mobile devices, computers the internet to provide high-quality healthcare services. E-healthcare decision support have been developed optimize services enhance a patient’s health. These enable rapid access specialized via reliable information, retrieved from cases or patient histories. This phenomenon reduces time taken by patients physically visit institutions. current research work, new Shuffled Frog Leap Optimizer with Deep Learning-based Decision Support System (SFLODL-DSS) is designed for diagnosis of Cardiovascular Diseases (CVD). The aim proposed model identify classify cardiovascular diseases. SFLODL-DSS technique primarily incorporates SFLO-based Feature Selection (SFLO-FS) approach feature subset election. For purpose classification, Autoencoder Gated Recurrent Unit (AEGRU) exploited. Finally, Bacterial Foraging Optimization (BFO) algorithm employed fine-tune hyperparameters involved in AEGRU method. To demonstrate enhanced performance technique, series simulations was conducted. simulation outcomes established superiority it achieved highest accuracy 98.36%. Thus, can be exploited proficient tool future detection classification CVD.