Guided Growth: Machine Learning for Age-Specific Essentials, Health, and Lifestyle for Children

Guided Growth is a machine learning system that supports parents and caregivers in providing age-specific recommendations for children aged 0-10 years. The system automates the process of recommending essential documents, health requirements, and lifestyle changes based on the child’s age. Experimental results show that the platform’s machine learning engine successfully provides personalized recommendations, improving the accuracy and timeliness of health and developmental suggestions. These results demonstrate significant improvements over traditional methods, especially in minimizing missed milestones and enhancing user satisfaction. The system’s effectiveness in real-world applications proves its potential to revolutionize child care by providing automated, accurate, and relevant guidance for parents and caregivers.

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Publication Details

Published
2025-05-21
DOI
https://doi.org/10.1109/icaiss61471.2025.11041739
Primary Topic
Health, Environment, Cognitive Aging
Type
article
Field-Weighted Citation Impact
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article

Guided Growth: Machine Learning for Age-Specific Essentials, Health, and Lifestyle for Children

Surapaneni Phani Praveen, J. Refonaa, S. L. Jany Shabu, S. Dhamodaran et al.
Health, Environment, Cognitive Aging
article

Guided Growth: Machine Learning for Age-Specific Essentials, Health, and Lifestyle for Children

Surapaneni Phani Praveen, J. Refonaa, S. L. Jany Shabu, S. Dhamodaran, S P Divyadarshini, Thasleema Taj T
article en

Abstract

Guided Growth is a machine learning system that supports parents and caregivers in providing age-specific recommendations for children aged 0-10 years. The system automates the process of recommending essential documents, health requirements, and lifestyle changes based on the child’s age. Experimental results show that the platform’s machine learning engine successfully provides personalized recommendations, improving the accuracy and timeliness of health and developmental suggestions. These results demonstrate significant improvements over traditional methods, especially in minimizing missed milestones and enhancing user satisfaction. The system’s effectiveness in real-world applications proves its potential to revolutionize child care by providing automated, accurate, and relevant guidance for parents and caregivers.

Sathyabama Institute of Science and Technology (IN)
Openalex Percentile: Top 81%
Health, Environment, Cognitive Aging
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