Machine Learning in Pediatric Growth Assessment

This article explores the transformative role of machine learning (ML) in assessing pediatric growth, with a specific focus on the early diagnosis and continuous monitoring of growth disorders. An innovative ML algorithm, developed using logistic regression, is introduced to identify anomalies in child growth by leveraging biometric data. This original approach represents a significant advancement in the early detection of growth disorders, opening new avenues for precision medicine in pediatric care. While the results of this study are still being developed, the preliminary findings suggest that ML offers substantial opportunities to enhance the accuracy and efficiency of pediatric growth assessments.