Abstract:

With the rise of personalized health, the global personalized nutrition market is projected to reach $16.4 billion by 2027, driven by increasing health awareness and the desire for customized dietary solutions. Despite advancements in health monitoring, individuals still face challenges in determining their exact nutritional needs based on personal health data, with over 70% relying on generic dietary guidelines. The existing systems are limited by their dependence on manual calculations and generalized dietary frameworks. In this study, we propose a deep learning-based regression model to estimate individual nutritional density based on personalized health data, including biomarkers, dietary patterns, and activity levels. Our approach involves preprocessing health data through normalization, missing data imputation, and feature extraction, followed by training a deep learning model for precise nutrition density prediction. The proposed model aims to address the gap in current methods by offering an adaptive, data-driven solution for personalized dietary recommendations.

Description:

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