CLUSTERING AND ENSEMBLE LEARNING FOR ACCURATE PREDICTION OF NUTRITIONAL HEALTH OUTCOMES IN CHILDREN'S FOODS

Jeyanthi, P and Durga, R. (2025) CLUSTERING AND ENSEMBLE LEARNING FOR ACCURATE PREDICTION OF NUTRITIONAL HEALTH OUTCOMES IN CHILDREN'S FOODS. In: National Article on Mixed Methodology Souvenir 2025. Payanam.

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Abstract

Nutrients are essential for children's and teenagers' growth, development, and overall well-being. However, most children's diets are of poor quality, with parents struggling to ensure balanced nutrition. Eating habits formed in childhood often persist into adulthood, influencing long-term health outcomes. Studies show that many American children and adolescents fail to meet nutritional standards. Modern dietary patterns reveal increased snacking, soft drink consumption, and processed food intake. Childhood obesity and overweight have become major public health concerns, affecting about 15% of American youths. Many overweight children face risks of diabetes and cardiovascular issues. Digital nutrition interventions show promise but need validated best practices. Malnutrition, caused by deficiencies in essential nutrients, remains a global issue, particularly in low-income countries. Children represent the most vulnerable group, with millions worldwide lacking adequate nutrition.

Item Type: Book Section
Subjects: Computer Science > Statistical Methods
Domains: Computer Science
Depositing User: Mr IR Admin
Date Deposited: 07 May 2026 12:13
Last Modified: 11 May 2026 05:26
URI: https://ir.vistas.ac.in/id/eprint/13931

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