Kidney Failure Identification Using Augment Intelligence and IOT Based on Integrated Healthcare System

Internet of Things (IoT) and machine learning technology integration has had a significant positive impact on contemporary healthcare systems. The main objectives of this project are to develop and evaluate an integrated healthcare system based on the Internet of Things for the diagnosis and treatment of kidney-related illnesses. The system, which also uses a variety of sensors to continuously track essential health data, enables real-time communication between patients and medical professionals. Five machine learning models—Artificial Neural Networks (ANN), k-Nearest Neighbours (KNN), Support Vector Machine (SVM), Naive Bayes (NB), and Linear Regression (LR)—have been developed to predict patient health outcomes based on sensor data. Performance metrics and confusion matrices demonstrate the remarkable abilities of these models, with ANN standing out as a top performer. By combining IoT and machine intelligence, healthcare professionals can manage their patients’ treatment proactive and intervene early. This study highlights the revolutionary potential of machine learning and the internet of things to improve patient outcomes, monitor kidney health more effectively, and cut healthcare costs. As healthcare systems develop, the use of IoT and machine learning to manage diseases will revolutionise patient care.