Random Forest Predictive Model for Ventilator-Associated Pneumonia Prediction with IoT Data Analytics

To identify and forecast ventilator-associated pneumonia (VAP) early on, this study investigates the use of random forest predictive models in combination with Internet of Things (IoT) data analytics. Patients on mechanical ventilation are particularly vulnerable to VAP, a serious nosocomial infection. It uses Random Forest algorithms to examine a wide range of factors, such as vital signs, patient demographics, and ventilator settings, by using the abundance of healthcare data supplied by the IoT. The goal is to create reliable prediction models that can identify people who are at risk of getting VAP before any symptoms appear. Using data gathered from ventilator-integrated real-time monitoring devices, it creates a complete dataset. The findings reveal that random forest models are reliable and very accurate in predicting VAP. Integrating analytics from the IoT improves prediction accuracy, which in turn helps healthcare providers make better, timelier preventative decisions. Improving patient outcomes and optimizing resource allocation in critical care units may be possible as a result of this study's contributions to the expanding body of work on using sophisticated data-driven methodologies for infectious illness prediction in clinical settings.