Feature Selection and Prediction of Pediatric <i>Tuina</i> in Attention Deficit/Hyperactivity Disorder Management: A Machine Learning Approach Based on Parent-Reported Children's Constitution.

ABSTRACT: Background: Attention Deficit/Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder in children. Pediatric tuina, a traditional Chinese medicine (TCM) intervention, has shown potential in managing ADHD symptoms. Integrating machine learning (ML) into pediatric tuina could refine treatment personalization, allowing for a more feasible and better parent-administered use. Methods: We employed an ML-based model to analyze parent-reported constitutional features from 1005 children diagnosed with ADHD to predict individualized pediatric tuina treatments. This study focused on feature selection and the application of several ML models, including Support Vector Machines (SVM), Logistic Regression (LR), Multilayer Perceptron (MLP), an

SUBMITTER: Chen SC 

PROVIDER: S-EPMC12561778 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature