Advancing Pediatric Growth Assessment with Machine Learning: Overcoming Challenges in Early Diagnosis and Monitoring.

Background: Pediatric growth assessment is crucial for early diagnosis and intervention in growth disorders. Traditional methods often lack accuracy and real-time decision-making capabilities This study explores the application of machine learning (ML), particularly logistic regression, to improve diagnostic precision and timeliness in pediatric growth assessment. Logistic regression is a reliable and easily interpretable model for detecting growth abnormalities in children. Unlike complex machine learning models, it offers parsimony in transparency, efficiency, and reproducibility, making it ideal for clinical settings where explainable, data-driven decisions are essential.

Methods: A logistic regression model was developed using R to analyze biometric and demographic data from a cross-sectional dataset, including real-world data from public institucions. The study employed a bibliometric analysis to identify key trends and incorporated data preprocessing techniques such as cleaning, imputation, and feature selection to enhance model performance. Performance metrics, including accuracy, sensitivity, and the Receiver Operating Characteristic (ROC) curve, were utilized for evaluation.

Results: The logistic regression model demonstrated an accuracy of 94.65% and a sensitivity of 91.03%, significantly improving the identification of growth anomalies compared to conventional assessment methods. The model's ROC curve showed an area under the curve (AUC) of 0.96, indicating excellent predictive capability. Findings highlight ML's potential in automating pediatric growth monitoring and supporting clinical decision-making, as it can be very simple and highly interpretable in clinical practice.

Conclusions: ML, particularly logistic regression, offers a promising tool for pediatric healthcare by enhancing diagnostic precision and operational efficiency. Despite these advancements, challenges remain regarding data quality, clinical integration, and privacy concerns. Future research should focus on expanding dataset diversity, improving model interpretability, and conducting external validation to facilitate broader clinical adoption.