Ai-Driven Personalized Nutrition based on Blood Biomarker Analysis: A Preventive Healthcare and Meal Delivery Framework

Abstract

The increasing prevalence of lifestyle-related diseases such as diabetes, obesity, and cardiovascular disorders has highlighted the limitations of generic, one-size-fits-all dietary recommendations. Individual variations in metabolism and nutritional needs necessitate more precise and personalized dietary interventions. In this context, the present study proposes an AI-driven personalized nutrition framework based on blood biomarker analysis as a preventive healthcare solution integrated with a meal delivery system. The primary objective of the study is to design and evaluate a technology-enabled model that translates clinical health data into actionable dietary recommendations. The study adopts a descriptive and exploratory research methodology, utilizing an AI framework incorporating OCR, NLP, and machine learning techniques for blood report interpretation, supported by survey-based feasibility analysis. The key findings indicate high user acceptance of AI-based diet recommendations, significant difficulty in understanding blood reports without technological support, and strong willingness to adopt personalized meal delivery services. The proposed framework demonstrates practical value by improving dietary adherence, convenience, and preventive health engagement. The study concludes that integrating artificial intelligence with biomarker-based nutrition and meal delivery can offer a scalable, user-centric approach to preventive healthcare, particularly relevant in emerging health-tech ecosystems.