Year: 2025
Type: article
Abstract: Personalized nutrition has gained significant attention in recent years due to its potential to enhance individual health outcomes. This project aims to develop a machine learning-based system that provides customized nutrition recommendations using the Random Forest algorithm. The system utilizes diverse datasets containing dietary habits, health conditions, and demographic information to generate personalized diet plans. The methodology involves data collection, preprocessing, feature selection, model training, and evaluation usingkeyperformance metrics such as accuracy, precision, and recall. Findings indicate that the Random Forest algorithmic actively predicts optimal dietary recommendations based on individual health profiles with high accuracy. Despite promising results, limitations such as dataset biases and model interpretability pose challenges. The study concludes by discussing the practical applications of personalized nutrition systems and potential future improvements, including integrating deep learning techniques for enhanced accuracy.
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W4410128679