Intelligent Machine Learning-Based Personalized Nutrition Recommendation for Women

By Trisha

Personalized nutrition is increasingly influenced by advances in data science, artificial intelligence, and machine learning. Intelligent Machine Learning-Based Personalized Nutrition Recommendation for Women provides a focused technical examination of how computational methods can be applied to nutrition recommendation and individualized dietary decision support. The book brings together machine learning, nutrition science, data analysis, recommendation systems, health informatics, and personalized dietary modeling within a structured interdisciplinary framework.

The book introduces the foundations of personalized nutrition and explains the importance of considering individual characteristics when developing dietary recommendations. Factors such as nutritional requirements, dietary patterns, food preferences, lifestyle characteristics, demographic information, and other relevant personal variables can influence the suitability of a nutrition recommendation. The text examines these considerations from a computational and nutrition-science perspective.

A central focus is placed on machine learning-based recommendation methods. The book discusses how structured nutrition and user-related data can be represented, processed, and analyzed to identify patterns and generate personalized recommendations. Concepts including data preprocessing, feature selection, classification, prediction, clustering, recommendation models, model training, and evaluation are considered in relation to intelligent nutrition systems.

The book further explores the relationship between machine learning and personalized dietary recommendations for women. Women's nutritional needs can vary across different stages of life and according to individual circumstances, making personalization an important consideration in computational nutrition systems. The discussion examines how user characteristics and dietary information can be incorporated into recommendation frameworks while emphasizing the importance of appropriate data representation and model evaluation.

• Format: Paperback
• Language: English
• ISBN: 1088200036
• ISBN13: 9781088200032
• Release Date: August 2026
• Publisher: Nala Kala
• Length: 114 Pages
• Weight: 0.36 lbs.
• Dimensions: 9.0" x 0.2" x 6.0"