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Applied Sciences
Volume 15
Issue 17
10.3390/app15179283
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

An Integrated AI Framework for Personalized Nutrition Using Machine Learning and Natural Language Processing for Dietary Recommendations

by
Sena Karamanlı Aydın
1,
Raja Hashim Ali
1,2,
Shan Faiz
1 and
Talha Ali Khan
1,*
1
Department of Business, University of Europe for Applied Sciences, Think Campus, Konrad-Zuse-Ring 11, 14469 Potsdam, Germany
2
Faculty of Computer Science and Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi 23460, Pakistan
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(17), 9283; https://doi.org/10.3390/app15179283 (registering DOI)
Submission received: 20 July 2025 / Revised: 17 August 2025 / Accepted: 20 August 2025 / Published: 23 August 2025
(This article belongs to the Special Issue Artificial Intelligence for Healthcare: Technologies, Applications, and Impact)
Versions Notes

Abstract

Nutrition plays a pivotal role in preventive health, yet existing digital solutions often lack personalization and accessibility. This study presents an AI-driven framework that integrates machine learning (ML) and natural language processing (NLP) to deliver dynamic, user-centric dietary recommendations. A gradient boosting model, trained on NHANES demographic and anthropometric data, predicts caloric needs with an MAE of 132 kcal, while a locally deployed LLM (Mistral 7B) interprets free-text dietary constraints with 91% accuracy. Rule-based filtering from the USDA database ensures nutritional balance. A pilot usability test (n = 5) confirmed the system’s practicality and satisfaction. The proposed framework addresses key gaps in scalability, privacy, and adaptability, demonstrating the potential of hybrid AI techniques in applied nutrition science. By bridging computational methods with food science, this work offers a reproducible, modular solution for personalized health applications.
Keywords: personalized nutrition; machine learning; natural language processing; dietary recommendations; NHANES dataset; large language models (LLMs); rule-based filtering; health informatics personalized nutrition; machine learning; natural language processing; dietary recommendations; NHANES dataset; large language models (LLMs); rule-based filtering; health informatics

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MDPI and ACS Style

Aydın, S.K.; Ali, R.H.; Faiz, S.; Khan, T.A. An Integrated AI Framework for Personalized Nutrition Using Machine Learning and Natural Language Processing for Dietary Recommendations. Appl. Sci. 2025, 15, 9283. https://doi.org/10.3390/app15179283

AMA Style

Aydın SK, Ali RH, Faiz S, Khan TA. An Integrated AI Framework for Personalized Nutrition Using Machine Learning and Natural Language Processing for Dietary Recommendations. Applied Sciences. 2025; 15(17):9283. https://doi.org/10.3390/app15179283

Chicago/Turabian Style

Aydın, Sena Karamanlı, Raja Hashim Ali, Shan Faiz, and Talha Ali Khan. 2025. "An Integrated AI Framework for Personalized Nutrition Using Machine Learning and Natural Language Processing for Dietary Recommendations" Applied Sciences 15, no. 17: 9283. https://doi.org/10.3390/app15179283

APA Style

Aydın, S. K., Ali, R. H., Faiz, S., & Khan, T. A. (2025). An Integrated AI Framework for Personalized Nutrition Using Machine Learning and Natural Language Processing for Dietary Recommendations. Applied Sciences, 15(17), 9283. https://doi.org/10.3390/app15179283

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