Optimal Drug Dosage Control in Immune Systems Using Machine Learning

Abstract: This study combines supervised machine learning and reinforcement learning techniques to present a novel framework for individualized drug dosage prediction. To suggest dosage levels that maximize recovery while minimizing side effects, we have devised a system that evaluates patient-specific characteristics, such as the severity of the illness, the immune system's reaction, and the present drug concentration. Our hybrid strategy combines adaptive reinforcement learning systems with conventional predictive models that can change dose in real time. According to testing data, our method significantly improves predicted recovery outcomes over traditional protocols, achieving 92% accuracy in dosage estimates. To help medical professionals make evidence-based dosing decisions, we have integrated these algorithms into an intuitive web interface. By creating a flexible framework that can be used in a variety of therapeutic situations outside of our current implementation, this study contributes to the developing field of AI-enhanced precision medicine. Keywords: Deep Learning, Precision Medicine, Drug Dosage Optimization, Machine Learning, Reinforcement Learning, and Healthcare Applications