Vital: An AI-Powered Dietary Supplement Recommender System

Healthcare costs are rising globally, making personalized and affordable preventive healthcare increasingly important. Dietary supplements help individuals maintain health, but selecting the right supplement is challenging due to overwhelming product choices and inconsistent online information. To address this problem, we developed Vital, an AIpowered dietary supplement recommender system that generates personalized supplement suggestions based on user age, gender, allergies, and natural-language health goals. Vital integrates MERN stack development with machine learning, using sentiment analysis and intent extraction to interpret user descriptions. The system also applies rule-based filtering to identify safe supplements, avoiding allergens or age-inappropriate products. Experimental results show that Vital achieves up to 93% accuracy in understanding user intent and producing relevant recommendations. Vital significantly reduces the time, cost, and confusion associated with supplement selection. This research presents the system architecture, methodology, and evaluation of Vital as a scalable AI tool for preventive healthcare.