AI and Deep Learning-Based Intelligent Drug Recommendation System for Patient Health Monitoring in IoT-Enabled Healthcare

Abstract

The integration of Artificial Intelligence (AI), Deep Learning (DL), and the Internet of Things (IoT) is transforming healthcare by enabling personalized drug recommendations and real-time patient monitoring. This study introduces an Intelligent Drug Recommendation System that leverages Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs) to analyze sequential and complex health data, combined with Genetic Algorithm (GA) optimization for tailoring drug recommendations to individual patient profiles. The system utilizes IoT-enabled wearable devices to continuously monitor patient metrics, including heart rate, glucose levels, and blood pressure, which are preprocessed using normalization and standardization techniques. LSTM networks capture time-based dependencies, CNNs extract critical health data features, and GA ensures optimized drug selection based on efficacy, side effects, and patient compliance. A real-time feedback loop enables dynamic adjustments to treatment regimens as patient conditions evolve. Results demonstrate a 30% improvement in drug recommendation accuracy and a 25% reduction in emergency medical services (EMS) response times. These advancements highlight the system’s ability to enhance patient care by delivering precise and timely medication suggestions. Data security is ensured through cloud-based storage accessible only to authorized personnel, safeguarding patient privacy. This study underscores the transformative potential of AI and IoT-driven systems in healthcare, paving the way for more personalized, responsive, and efficient solutions to improve patient outcomes.