AI assissted Anesthesia Machine

The Personalized Anesthesia Management project focuses on predicting postoperative complications in patients undergoing surgery, utilizing machine learning algorithms such as Support Vector Machine (SVM), Random Forest, and XGBoost. The dataset provides detailed information about patients, including demographic data, surgery type, anesthesia used, surgical duration, and postoperative conditions. The primary goal of the project is to develop a predictive model that can assess the likelihood of complications, such as nausea, mild bleeding, respiratory distress, or delayed recovery, based on preoperative and intraoperative factors. The outcome of the surgery is classified into two categories: 0 (no complications) and 1 (complications present). This model aims to assist healthcare providers in managing patient anesthesia more effectively by identifying high-risk patients and optimizing perioperative care, thus improving surgical outcomes and enhancing patient safety. The system is implemented with a frontend in HTML, CSS, and JavaScript, and the backend is powered by Python, ensuring a user-friendly interface and robust prediction capabilities.   Keywords: Anesthesia management, postoperative complications, machine learning, SVM, Random Forest, XGBoost, personalized care, surgery prediction, patient safety.