Abstract: In the age of customized medicine, getting the right drug to the right patient at the right time is a major challenge. This research describes the creation of an Adaptive medicine Recommendation System that employs Reinforcement Learning (RL) to improve medicine prescription accuracy based on unique patient characteristics. Unlike standard drug recommendation systems, which rely on static rules or supervised learning models, the proposed system represents the treatment process as a Markov Decision Process (MDP) and using RL approaches to learn optimal drug strategies over time. The system generates dynamic and tailored drug recommendations based on patient-specific data such as age, medical history, present symptoms, and ongoing drugs. It is constantly learning and adapting based on feedback from patient results, making it resistant to changing health circumstances. The RL agent is trained and tested using benchmark healthcare datasets, and its performance is compared to traditional methods in terms of accuracy, flexibility, and safety. The findings show that the suggested approach improves clinical decision-making while also paving the way for intelligent, real-time, and patient-centric healthcare solutions.
Keywords: Reinforcement learning, Markov Decision Process, Tailored drug, centric healthcare solutions.
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DOI:
10.17148/IJARCCE.2025.14732
