The BAYESIAN NETWORKS AND ATTENTION MECHANISMS FOR CHRONIC KIDNEY DISEASE PREDICTION IN IOMT SYSTEMS: AN AI AND ROBOTIC AUTOMATION APPROACH

AI AND ROBOTIC AUTOMATION APPROACH

Abstract

The prediction model of Chronic Kidney Disease (CKD) in IoMT systems introduced here is a fusion between Bayesian Networks and Attention Mechanisms. It enhances diagnostic accuracy and healthcare decision making by employing advanced AI methodologies through real-time patient data. Compared to traditional methods, such as Stochastic Gradient Boosting or Multiverse Jackal Optimization, the model exhibits high precision and recall rates (see performance results) A cost-effective technique that can work in real-time for IoMT ecosystems, and enhance healthcare automation by offering more specific CKD forecasts.