{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["12(10)"],"submitter":["Chen SC"],"pubmed_abstract":["<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), an"],"journal":["Bioengineering (Basel, Switzerland)"],"pagination":["1012"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12561778"],"repository":["biostudies-literature"],"pubmed_title":["Feature Selection and Prediction of Pediatric &lt;i&gt;Tuina&lt;/i&gt; in Attention Deficit/Hyperactivity Disorder Management: A Machine Learning Approach Based on Parent-Reported Children's Constitution."],"pmcid":["PMC12561778"],"pubmed_authors":["Wu GT","Li H","Ren G","Han LF","Lo KC","Qin J","Zhang X","Chen SC","Wong PM","Li ZH","Yeung WF"],"additional_accession":[]},"is_claimable":false,"name":"Feature Selection and Prediction of Pediatric &lt;i&gt;Tuina&lt;/i&gt; in Attention Deficit/Hyperactivity Disorder Management: A Machine Learning Approach Based on Parent-Reported Children's Constitution.","description":"<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), an","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Sep","modification":"2026-05-14T03:19:57.788Z","creation":"2026-05-14T03:12:40.397Z"},"accession":"S-EPMC12561778","cross_references":{"pubmed":["41155011"],"doi":["10.3390/bioengineering12101012"]}}