Data Driven Health Education and Patient Engagement Using Advanced AI Algorithms

Abstract

This research examines AI approaches for tailored health education and patient engagement, concentrating on hybrid collaborative filtering and knowledge-based filtering (Hybrid CF-CB). The research compares six AI systems' memory, accuracy, precision, F1 score, interest score, and working speed. Hybrid CF-CB outperforms collaborative filtering, content-based filtering, AI chatbots, sentiment analysis, and reinforcement learning in accuracy, engagement, and content relevance. The hybrid CF-CB technique uses collaborative and content-based filtering to provide particular ideas that engage patients over time. The algorithm also adjusts swiftly to changing user preferences, refining its estimates to increase short-term and long-term engagement. This is distinct from AI chatbots, which are speedy but not effective at retaining people. This research illustrates that mixed recommendation models may help healthcare by personalizing health education and patient engagement. The statistics suggest that the Hybrid CF-CB method improves patient outcomes by providing valuable, entertaining, and rapid information that adapts with their requirements.

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