Ai driven Drug alert system

The increasing adoption of artificial intelligence (AI) in the healthcare sector has brought forth significant advancements in drug management, enhancing decision-making processes. This project introduces an AI-driven Drug Alert System designed to predict the approval status of drugs based on key features such as approval year, dosage, price, drug age, and manufacturer approval rates. The system utilizes machine learning algorithms, including Support Vector Machines (SVM), Random Forest, and XGBoost, to classify drug approval status into three categories: 'Approved', 'Rejected', and 'Pending'. These algorithms are trained on various features such as dosage in mg, price in USD, approval status, and drug characteristics, with a focus on providing actionable insights to healthcare professionals. The proposed system offers a scalable solution to improve drug approval processes, ensuring timely alerts and facilitating more informed decision-making. The results of the system’s evaluation demonstrate the potential for accurate classification, leveraging machine learning techniques to automate drug approval monitoring and minimize human intervention. The project also emphasizes the importance of using advanced AI models to enhance efficiency and precision in pharmaceutical decision-making.

Keywords: AI-driven Drug Alert System, Machine Learning, SVM, Random Forest, XGBoost, Drug Approval Status, Healthcare Decision Support, Classification, Pharmaceutical Intelligence, Approval Prediction.