An IoT-Based CNN Model for Patients in ICU Beds During the COVID-19 Outburst

The rising number of hospital admissions is a worldwide cause for alarm, with major contributors being an ageing population, the prevalence of diseases, and the current COVID-19 epidemic. IoT provides the scalability necessary for this determination, enabling incessant and reliable health monitoring on a worldwide scale, and therefore easing this difficulty. The COVID-19 virus has quickly disseminated all across the globe. Healthcare practitioners around the world face a number of difficulties during the epidemic, one of the most significant being the management of available resources. Since disease, approximately patients will need to be self-confessed to the hospital with an immediate need for oxygen acute medical care, making it crucial to properly allocate the beds in the (ICU). Many nations' efforts to stop the spread of the deadly COVID-19 virus relied heavily on technology, especially Artificial Intelligence (AI). In this research, we modify a (CNN) to better detect the requirement for the ICU and the predicted length of stay among COVID-19 patients. A 895 patients and evaluate the CNN with Sun flower Optimization (SFO). The results of the studies disclose that the projected perfect for predicting ICU Length of Stay (LoS) achieves the maximum accuracy. It is believed that this contribution would pave the way for better resource management in healthcare institutions, ultimately leading to the saving of lives.