A Fair and Safe Usage Drug Recommendation System in Medical Emergencies by a Stacked ANN

The importance of online recommender systems for drugs, medical professionals, and hospitals is growing. Today, the majority people use consultations drug recommendations all types health issues. Emergencies such as pandemics, floods, or cyclones can be helped by system. In era machine learning (ML), produce more accurate, quick, reliable clinical predictions with minimal costs. As a result, these maintain better performance, integrity, privacy patient data in decision-making process provide precise information at any time. Therefore, we present stacked artificial neural network (ANN) model to improve fairness safety treatment infectious diseases. To reduce side effects, drugs are recommended based on patient’s previous profile, lifestyle, habits. proposed system produced results 97.5% accuracy. A this could useful recommending safe medicines patients, especially during emergencies.