Automated Nutritional Guidance for Obesity Management: Insights from Machine Learning, Naïve Bayes, Random Forest

A., Rupa and Ch. Akshaya, Reddy and E., Shravya and E., Akshaya and K., Rajasri (2025) Automated Nutritional Guidance for Obesity Management: Insights from Machine Learning, Naïve Bayes, Random Forest. Journal of Data Science, 2025 (04). pp. 1-14. ISSN 2805-5160

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Abstract

Obesity is a growing global health concern linked to numerous chronic diseases, requiring effective and personalized nutritional interventions. This study presents an automated nutritional guidance system designed to support obesity management through personalized diet recommendations. The system leverages user-specific data, including age, weight, height, activity level, and health goals, to generate tailored dietary plans using machine learning algorithms and nutrition databases. By integrating real-time feedback, food tracking, and adaptive meal suggestions, the platform aims to enhance user adherence and improve long-term outcomes. Preliminary evaluations suggest that automated guidance can offer scalable, cost-effective support while reducing reliance on continuous in-person consultations. The proposed system represents a promising advancement in digital health tools for obesity management. Obesity continues to pose a significant public health challenge worldwide, contributing to a range of non-communicable diseases such as type 2 diabetes, cardiovascular disorders, and certain cancers. Effective nutritional management is a cornerstone of obesity intervention, yet traditional approaches often face limitations related to accessibility, personalization, and long-term adherence. This paper presents the design and development of an Automated Nutritional Guidance System aimed at enhancing obesity management through intelligent, user-centered dietary recommendations. The system utilizes a combination of machine learning algorithms, nutritional databases, and user input to provide personalized dietary plans aligned with individual health goals, dietary preferences, and lifestyle patterns. Key features include real-time meal suggestions, nutrient tracking, behavior monitoring, and adaptive feedback mechanisms.

Item Type:	Article
Uncontrolled Keywords:	MachineLearning ,NaiveBayes, Random Forest.
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > RC Internal medicine T Technology > T Technology (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering
Depositing User:	Unnamed user with email masilah.mansor@newinti.edu.my
Date Deposited:	19 Jun 2025 09:44
Last Modified:	19 Jun 2025 09:44
URI:	http://eprints.intimal.edu.my/id/eprint/2143

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