Jul 2026· International Journal of Computer & Organization Trends· 0 citations· 10 references
TL;DR
This research proposes an AI-driven personalized diet recommendation system that generates customized diet plans based on user health information and demonstrates the ability to generate safe and personalized diet recommendations.
Abstract
Maintaining a balanced and nutritious diet is essential for preventing lifestyle-related diseases and enhancing overall well-being. However, conventions diet planning approaches often provide generalized guidelines that do not account for individual health conditions, allergies, lifestyle habits, or personal food preferences. Because of this, many individuals follow diet plans that may not be suitable for their specific nutritional needs. Recent advancements in artificial intelligence and machine learning have enabled systems to generate personalized recommendations. This research proposes an AI-driven personalized diet recommendation system that generates customized diet plans based on user health information. The system collects parameters such as age, gender, height, weight, dietary preferences, allergies, and medical conditions. A Random Forest algorithm is employed to determine the most appropriate diet category. A rule-based filtering mechanism is applied to ensure that the recommended diet follows medical guidelines and avoids foods that may be harmful. The system also calculates important health indicators such as Body Mass Index (BMI), Basal Metabolic Rate (BMR), and daily calorie requirements to support accurate nutritional analysis. In addition, an AI chat-bot is integrated to provide dietary guidance and answer user queries related to nutrition. The system is implemented as a web application using the Flask framework and demonstrates the ability to generate safe and personalized diet recommendations.
Artificial Intelligence (AI) is revolutionizing health and nutrition, significantly advancing personalized dietary management and health optimization. Technologies like machine learning algorithms, data mining, and predictive models enable the creation of individualized diet plans based on genetic data, lifestyle habits, and biometric information, offering more effective and precise nutrition recommendations. AI-enabled systems provide personalized nutritional strategies for these diseases, offering real-time dietary suggestions tailored to individual needs. Additionally, AI optimizes nutritionists' and healthcare providers' recommendations by analyzing the effects of nutrition programs on health outcomes. In the food industry, AI promises advancements in food safety, quality control, product innovation, and sustainable food production. However, the widespread adoption of AI in nutrition faces challenges, including ethical concerns, data privacy issues, and algorithmic biases. Protecting personal health data and ensuring AI systems are fed with accurate, unbiased data are crucial in addressing these challenges. This review highlights AI's potential in nutrition science and health management, identifying opportunities for future research and innovation.
Hatice Karakoç, Eda Çaycı, Seda Çiftçi· Journal of NutriLife· 0 citations
This paper presents the development of a diet plan generator system that uses user-specific parameters such as age, weight, gender, activity level, dietary goals, and food preferences to recommend a structured meal plan.
This paper introduces Allergist, a dietary recommendation system based on machine learning for people suffering from food allergies and associated nutritional risks. As many as 1-10% of the population are estimated to have a food allergy, while navigation of everyday eating choices continues to be challenging due to ambiguous food labelling and a constant risk of cross-contamination. There are many mobile health applications in existence that, as reviewed, do not provide verified information, personalization or real-time assistance. The system proposed in this paper aims to tackle these shortcomings with a single web platform that integrates a multi-model allergen classifier, an autoencoder-based recommender, and a conversational assistant. Three classifiers (Random Forest, Gradient Boosting, and a Neural Network) were trained and evaluated on a curated collection of 54,697 recipes, each described using 883 ingredients and 54 allergen classes. Gradient Boosting had the highest micro-averaged F1 score of 100.0 percent, followed by the Neural Network with 99.5 percent, and then Random Forest at 91.2 percent. Each recipe was embedded in a 64-dimensional latent space using the autoencoder, and the adoption of cosine similarity over these results led to personalized and allergen-safe suggestions. The initial evaluation with 10 users resulted in positive scores for usability and perceived safety. The findings of this report indicate that an integrated multi-model approach could enhance the trustworthiness and availability of digital allergy support, thereby aligning with Sustainable Development Goal 3.
Felicia Wong Yu Zhen, Maythem K. Abbas· 2026 International Conferenc...· 0 citations
Experimental results demonstrate that the proposed PLAN'EAT Nutrition Advisor can efficiently generate personalized, nutritionally compliant, and diverse weekly meal plans while maintaining transparent expert-rule-driven optimization.
Dimitris Tsolakidis, Vasilis Stamatis, L. Gymnopoulos et al.· Frontiers in Nutrition· 0 citations
The ClinicalML uses advanced machine learning algorithms to analyze patient data and identify health conditions accurately using important parameters like age, BMI, blood pressure, and glucose levels to assist in early disease prediction and personalized treatment support.
G. Vamsi, K. Devendra· International Scientific Jou...· 0 citations
Findings indicate that the proposed architecture is capable of generating reliable and consistent educational recommendations while reducing redundant AI requests through recommendation caching, and contributes to the development of intelligent health information systems.
Nasrul, Henry Saptono, Rusmanto· Komputasi· 0 citations
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