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

<b><i>Background</i></b>: Attention Deficit/Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder in children. Pediatric <i>tuina</i>, a traditional Chinese medicine (TCM) intervention, has shown potential in managing ADHD symptoms. Integrating machine learning (ML) into pediatric <i>tuina</i> could refine treatment personalization, allowing for a more feasible and better parent-administered use. <b><i>Methods</i></b>: We employed an ML-based model to analyze parent-reported constitutional features from 1005 children diagnosed with ADHD to predict individualized pediatric <i>tuina</i> 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), and Random Forest (RF). The key task involved identifying the most relevant features for effective TCM pattern identification and diagnosis, which would guide personalized treatment strategies. <b><i>Results</i></b>: The ML models displayed strong predictive performance, with the MLP model achieving the highest Area Under the Curve (AUC) of 0.90 and an accuracy (ACC) of 0.74. Seven features were selected five times in cross-validation. This facilitated a more targeted and effective pediatric <i>tuina</i> application tailored to individual constitution. <b><i>Conclusion</i></b>: This study developed an ML-based approach to enhance ADHD management in children using pediatric <i>tuina</i>, informed by a parent-reported questionnaire. It identified seven key features for TCM pattern identification and personalized treatment strategies. MLP achieved the highest AUC and ACC.