Predicting Postoperative Complications Using Machine Learning on Anesthesia CareNet Data

The Anesthesia Complication Predictor project demonstrates how AI and ML can enhance clinical judgment and patient safety in the medical field. The system successfully predicts the likelihood of complications during or after surgery by evaluating patient and operation-related data such as age BMI type of surgery anesthesia type pain level and medical notes. Precision and dependability in prediction results are ensured by combining a variety of machine learning algorithms such as Random Forest XGBoost and Logistic Regression   with     sophisticated preprocessing  techniques  like standardization one-hot encoding and TF- IDF vectorization. In addition to prediction the system offers an intuitive platform that helps end users and administrators alike. While the user module enables patients and medical staff to easily register log in and receive real time predictions the admin module enables safe user administration and health advice. The system becomes a thorough and useful anesthetic risk assessment solution when predictive analytics and digital health management are combined. All things considered the project achieves its objective of offering data-driven insights to assist anesthesiologists and other medical professionals with patient monitoring and preoperative planning. It serves as an example of how machine learning could help lower medical risks enhance evidence-based decision-making and encourage safer surgical outcomes. The Anesthesia Complication Predictor possesses the capacity to be a vital tool in modern healthcare practice with additional improvement and integration into hospital systems.

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