Enhancing patient information performance in internet of things-based smart healthcare system: Hybrid artificial intelligence and optimization approaches

Abstract

Utilizing cutting-edge technology in the healthcare industry could provide advanced methods for managing medical records for patients’ treatment. Integrating essential technologies like artificial intelligence (AI) can enhance healthcare services. The intersection of the Internet of Things (IoT) and AI presents various prospects within the healthcare industry. This research paper presents a novel model for enhancing the standard of treatment in smart healthcare systems (SHS) based on the convergence of AI and IoT. We investigate optimizing patient data processing performance in smart healthcare centers. The upgraded particle swarm optimization-long short-term memory (PSO-LSTM) algorithm is introduced to optimize the IoT-based SHS model. In order to perform a more suitable classification of the patient medical data, PSO is determined to be compared with PSO-LSTM to tune several metrics and benchmarks to achieve the highest value on patient data process performance. The proposed outcomes discovered that in terms of the test sets, the predicted values of patient health risks, and just six of them are varied, with an accuracy of 92.5%. This accuracy indicates the PSO-LSTM algorithm has more satisfactory performance and higher efficiency and provides a more secure, reliable, and improved overall patient satisfaction experience.