DEVELOPMENT OF A MOBILE APP FOR PERSONALIZED NUTRITION PLANNING USING DEEP LEARNING MODELS

Abstract

Abstract: In the evolving field of personalized nutrition, mobile applications equipped with advanced technologies are increasingly important for delivering tailored dietary advice. This paper explores the development of a mobile app that leverages deep learning models to provide personalized nutrition planning. The app integrates user-specific health data, including age, gender, activity level, and health conditions, to generate customized dietary recommendations. By employing neural networks and other deep learning techniques, the app aims to enhance the accuracy and relevance of nutrition advice, addressing the limitations of generic dietary suggestions. The development process involved data collection from various sources, model training, and app design to ensure a user-friendly interface and real-time feedback. Evaluation through user testing demonstrated the app's effectiveness in improving dietary adherence and user satisfaction. The results underscore the potential of deep learning to revolutionize personalized nutrition by offering precise and actionable recommendations. This research contributes to the growing body of knowledge on mobile health technologies and their application in personalized dietary interventions, highlighting future directions for enhancing app functionality and expanding its impact on user health outcomes.