Diet Recommendation Systems Using Machine Learning: Approaches, Challenges, and Future Directions
Year: 2024
Type: article
Abstract: A greater emphasis on health and wellness has emerged in recent years, and many people find it difficult to stick to balanced diets because of lifestyle choices or a lack of individualized guidance.The papers examined in this collection offer several methods for developing machine learning-based diet recommendations that offer individualized meal plans based on personal health information including height, age, BMI, and particular health objectives.These systems assess user input and produce customized diet and exercise regimens using algorithms such as K-Means Clustering, Random Forest, kNN, and Naive Bayes.The goal is to provide reasonably priced, accessible options that encourage better eating habits and enhance general wellbeing for those without access to qualified trainers or nutritionists.By automating and customizing diet plans, the suggested solutions have the potential to surpass conventional approaches and guarantee that users receive suggestions that are tailored to their individual dietary requirements and health goals.
Author: Shruthi Nagati
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4405657046