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Related Concept Videos

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Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by persistent inattention, hyperactivity, and impulsivity. It affects approximately 5-8% of children globally, with around 60-70% of cases persisting into adulthood. ADHD has significant implications for educational attainment, social interactions, and occupational success.
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Related Experiment Video

Updated: Jan 7, 2026

Event Related Potentials ERPs and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder ADHD
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Machine learning-guided feature selection and predictive model construction for attention-deficit/hyperactivity

Haojie Meng1, Songtao Li2, Xiwen Xing1

  • 1Department of Children Health Care, Children's Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.

Frontiers in Psychiatry
|January 2, 2026
PubMed
Summary

This study identified key indicators for Attention Deficit/Hyperactivity Disorder (ADHD) and developed a machine learning model using routine clinical data for early ADHD screening and decision support.

Keywords:
attention deficit/hyperactivity disordermachine learningroutine blood countsserum biochemical parameterssystemic inflammation markers

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Area of Science:

  • Neurodevelopmental disorders
  • Biomarkers and diagnostics
  • Machine learning in healthcare

Background:

  • Attention Deficit/Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder with diagnostic challenges.
  • Current diagnostic methods for ADHD are limited, necessitating novel approaches.
  • This research sought to identify new indicators and create a machine learning model for ADHD identification.

Purpose of the Study:

  • To identify potential candidate indicators for Attention Deficit/Hyperactivity Disorder (ADHD).
  • To construct an interpretable machine learning model for ADHD identification using routine clinical data.
  • To provide a tool for early screening and clinical decision support in ADHD.

Main Methods:

  • A cohort of 8,598 children was analyzed, categorized into ADHD, subthreshold ADHD (s-ADHD), and healthy controls (HC).
  • Data included demographics, blood counts, biochemical parameters, body composition, and inflammation markers.
  • Machine learning models, including LightGBM, were developed and interpreted using SHAP values to identify key predictors from 40 variables.

Main Results:

  • Significant differences in inflammatory markers, glucose, BMI, body fat, albumin, cholesterol, and lymphocyte counts were observed among the groups.
  • LASSO regression identified 11 core predictors, including age, RDW-SD, sex, calcium, glucose, and albumin.
  • The LightGBM model achieved high performance (AUC=0.924) in distinguishing ADHD from HC, but struggled to differentiate ADHD from s-ADHD.

Conclusions:

  • Routine clinical data can be leveraged to identify potential indicators for ADHD.
  • An interpretable, low-cost machine learning model shows promise for early ADHD screening.
  • The developed model can aid in clinical decision-making for ADHD diagnosis.