Jul 2026· 2026 6th International Conference on Inventive Computation and Information Technologies (ICICIT)· pp. 1616-1623· 0 citations· 12 references
Abstract
The increased rate of nutrition and lifestyle-related diseases has caused an urgent need for intelligent and personalised nutrition management systems. The traditional approaches to dietary recommendations are primarily based on universal meal planning guidelines without addressing specific health needs, requirements, preferences, fitness objectives, or restrictions on access to foods. As a result, in this paper, a web-based personalized nutrition recommendation framework based on Artificial Intelligence (AI) is proposed in order to overcome these drawbacks. The user health and nutrition datasets are first preprocessed by Robust Scaler (RS) to reduce the effect of outliers and to normalize the features. Then, the Autoencoder learns deep dietary features and dietary patterns. Moreover, Select from Model (SFM) selects the most important nutritional features to be optimized so as to achieve a higher recommendation efficiency and a higher accuracy in the prediction. Finally, the XGBoost model is deployed in the AI recommendation engine where it classifies dietary goals and provides personalized meal recommendations, sending them to the web dashboard, chatbot, and ordering module. The experimental results demonstrate the superiority of accuracy, precision, recall, F1-score, recommendation reliability and long-term dietary adherence when compared to a traditional approach.
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.
Usha Kamale, M. Pujashree, T.Siri Chandana et al.· International Journal of Com...· 0 citations
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
Nutritional deficiencies remain a significant global public health challenge, while existing dietary assessment and recipe recommendation systems often operate independently, limiting their ability to provide integrated and personalized nutritional guidance. This article presents an integrated artificial intelligence framework developed using Sri Lankan dietary reference intake guidelines, culturally specific food composition data, and a curated corpus of Sri Lankan recipes to support household nutritional guidance. The proposed framework was developed as part of this Sri Lankan study to address local nutritional challenges while providing a methodology that can be adapted to other countries by replacing country-specific dietary reference standards, food composition databases, and recipe repositories with those of other countries. The nutritional model employed adequacy ratio-based features with a random forest classifier, achieving 88.12% accuracy and a macro area under the curve of 0.91. The semantic module used Sentence-Bidirectional Encoder Representations from Transformers embeddings with fuzzy ingredient matching to achieve 86.84% classification accuracy under stratified cross-validation. By linking predicted deficiencies to context-aware recipes, the system transforms analytical insights into actionable meal recommendations. The results demonstrated that the framework achieved stable performance and showed strong potential for practical application.
L. Weerasinghe, Methusala Perera, Shahmi Mohamed et al.· Artificial Intelligence in H...· 0 citations
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.
S. Uma· 0 citations
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