Drug Recommendation System in Medical Emergencies using Machine Learning

In medical emergencies, rapid and precise drug recommendations are crucial for patient survival and effective treatment. This paper presents a Drug Recommendation System utilizing machine learning techniques to automate and optimize drug selection during critical medical situations. The system processes comprehensive patient data, including medical histories and real-time health indicators, to provide accurate drug recommendations. Advanced image processing, feature extraction, and classification algorithms form the core of the system, ensuring high accuracy and reliability. The system’s performance is validated using a confusion matrix, demonstrating its superiority over existing methodologies and its potential to enhance emergency medical care.