<p>This research study explores an advanced approach for predicting Alzheimer’s disease by integrating a bi-directional gated recurrent unit (GRU) attention model with a chatbot-driven questionnaire, enhanced by reinforcement learning. Our method employs a structured questionnaire to conduct cognitive evaluations based on users’ natural language responses. The GRU Attention Model, combined with reinforcement learning, refines the system’s ability to identify significant temporal dependencies within these responses, thus enhancing prediction accuracy. Reinforcement learning is utilized to optimize the interaction between the GRU model and chatbot, continuously improving the model’s performance based on user feedback and engagement. By merging natural language processing, GRU network architecture, and reinforcement learning, our approach aims to provide a more precise and detailed risk prediction for Alzheimer’s disease. This study investigates the synergy between GRU-based attention mechanisms, chatbot-driven user engagement, and reinforcement learning, evaluating how these elements collectively improve predictive models for assessing cognitive health in older adults. The findings highlight the method’s potential for early Alzheimer’s detection, achieving an impressive accuracy range of 93 to 100 percent and surpassing several recent methodologies in the field.</p>