Authors : Saili Sable; Prathmesh Malunjkar; Om Nagare; Shubhada Boraste; Sweety Patole

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/3yb4pym6

Scribd : https://tinyurl.com/yvpymp6y

DOI : https://doi.org/10.38124/ijisrt/26apr1034

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : 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.

Keywords : Artificial Intelligence, Dietary Supplements, Natural Language Processing, Personalized Recommendation Systems, Preventive Healthcare.

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