Personalized Flexible Meal Planning for Individuals With Diet-Related Health Concerns: System Design and Feasibility Validation Study

BACKGROUND: Chronic diseases such as heart disease, stroke, diabetes, and hypertension are major global health challenges. Healthy eating can help people with chronic diseases manage their condition and prevent complications. However, making healthy meal plans is not easy, as it requires the consideration of various factors such as health concerns, nutritional requirements, tastes, economic status, and time limits. Therefore, there is a need for effective, affordable, and personalized meal planning that can assist people in choosing food that suits their individual needs and preferences. OBJECTIVE: This study aimed to design an artificial intelligence (AI)–powered meal planner that can generate personalized healthy meal plans based on the user’s specific health conditions, personal preferences, and status. METHODS: We proposed a system that integrates semantic reasoning, fuzzy logic, heuristic search, and multicriteria analysis to produce flexible, optimized meal plans based on the user’s health concerns, nutrition needs, as well as food restrictions or constraints, along with other personal preferences. Specifically, we constructed an ontology-based knowledge base to model knowledge about food and nutrition. We defined semantic rules to represent dietary guidelines for different health concerns and built a fuzzy membership of food nutrition based on the experience of experts to handle vague and uncertain nutritional data. We applied a semantic rule-based filtering mechanism to filter out food that violate mandatory health guidelines and constraints, such as allergies and religion. We designed a novel, heuristic search method that identifies the best meals among several candidates and evaluates them based on their fuzzy nutritional score. To select nutritious meals that also satisfy the user’s other preferences, we proposed a multicriteria decision-making approach. RESULTS: We implemented a mobile app prototype system and evaluated its effectiveness through a use case study and user study. The results showed that the system generated healthy and personalized meal plans that considered the user’s health concerns, optimized nutrition values, respected dietary restrictions and constraints, and met the user’s preferences. The users were generally satisfied with the system and its features. CONCLUSIONS: We designed an AI-powered meal planner that helps people create healthy and personalized meal plans based on their health conditions, preferences, and status. Our system uses multiple techniques to create optimized meal plans that consider multiple factors that affect food choice. Our evaluation tests confirmed the usability and feasibility of the proposed system. However, some limitations such as the lack of dynamic and real-time updates should be addressed in future studies. This study contributes to the development of AI-powered personalized meal planning systems that can support people’s health and nutrition goals.