MACHINE LEARNING-BASED ANESTHESIA PREDICTION SYSTEM MODELING AND ANALYSIS FOR IMPROVED PATIENT MONITORING IN CLINICAL SETTINGS

Abstract

Abstract- The administration of anesthesia is a critical aspect of medical practice, where precise dosage levels are paramount to patient safety. However, determining the appropriate dosage can be challenging and may lead to adverse events if inaccuracies occur. To mitigate this risk, our project focuses on developing an advanced Anaesthesia Dosage Level Prediction system using machine learning techniques. Specifically, we leverage regression algorithms and boosting techniques to enhance the accuracy and efficiency of anesthesia dosage calculations.

Our proposed system offers a user-friendly interface tailored for medical professionals, allowing them to input patient data effortlessly and obtain accurate dosage level predictions promptly. By tapping into the capabilities of machine learning, our system can analyze patient parameters and predict optimal anesthesia dosage levels with a high degree of precision.To ensure patient safety and system reliability, rigorous testing and validation processes are integral parts of our project. We prioritize

accuracy and reliability, striving to minimize the risk of errors in dosage predictions. Through continuous refinement and validation, we aim to provide medical professionals with a dependable tool that optimizes anesthesia dosage calculations and enhances patient safety.

By improving the accuracy of anesthesia dosage predictions, our project has the potential to revolutionize medical practice, streamline workflows, and ultimately, minimize the occurrence of adverse events associated with anesthesia administration.