IoT Based ICU Healthcare: Optimizing Patient Monitoring and Treatment with Advanced Algorithms

Abstract

In the realm of IoT-based Intensive Care Unit (ICU) healthcare, the quest for precision and reliability in patient monitoring and treatment optimization is paramount. This study delves into the realm of advanced algorithms, particularly focusing on the Pelican Optimization Algorithm Long Short-Term Memory (POA-LSTM), known for its remarkable accuracy rates exceeding 95%. The POA-LSTM algorithm, fine-tuned through the Pelican Optimization Algorithm, emerges as a beacon of accuracy in ICU healthcare. By optimizing hyperparameters and leveraging the Pelican Optimization Algorithm's optimization prowess, POA-LSTM surpasses industry standards, offering unparalleled precision and recall rates. Its ability to make informed predictions and provide real-time insights significantly enhances the quality of patient care and clinical decision-making in ICU settings. Additionally, the study explores Context-Oriented Attention LSTM (COA-LSTM) and Particle Swarm Optimization Long Short-Term Memory (PSO-LSTM) algorithms, each contributing unique strengths to the landscape of IoT-based ICU healthcare. COA-LSTM's attention mechanism and PSO-LSTM's hyperparameter optimization further enrich the capabilities of predictive modeling and anomaly detection in critical care scenarios. Through the integration of these advanced algorithms, healthcare providers can harness the power of data-driven insights to revolutionize ICU healthcare, ensuring optimal patient outcomes and advancing the frontier of medical care in the digital age.