Intelligent Anesthesia Management System for Optimized Anesthesiologist Support and Complication Prevention

Abstract

The Personalized Anesthesia Management project focuses on predicting postoperative complications in patients undergoing surgery, through the use of advanced machine learning algorithms such as Support Vector Machine (SVM), Random Forest, and XG Boost. We can create complex models that inform predictive insights and data-driven decision-making. The dataset provides rich and detailed insights, delivering detailed information about patients, including demographic data, surgery type, anesthesia used, surgical duration, and postoperative conditions. The primary aim of the project is to develop and design a predictive model that will be capable of making true decisions about the possibility of postoperative complications like nausea, mild hemorrhage, respiratory issues, and extended recovery utilizing appropriate preoperative and intraoperative predictors. The result of the operation is classified into two types: 0 (no complications) and 1 (complications). The model is developed to help physicians better manage patient anesthesia by spotting dangerous patients and refining perioperative care, leading to enhanced surgical outcomes and improved patient safety. The system is constructed with a JavaScript, HTML, and CSS frontend and Python-based backend to have an interactive interface and accurate prediction power.