Machine learning-guided feature selection and predictive model construction for attention-deficit/hyperactivity disorder.

ABSTRACT:

Background

Attention Deficit/Hyperactivity Disorder (ADHD) is a highly prevalent neurodevelopmental disorder, but its diagnosis remains constrained. This study aimed to identify potential candidate indicators and construct an interpretable machine learning model for the identification of ADHD.

Methods

A total of 8,598 children were enrolled and classified into three groups: ADHD (n=3,678), subthreshold ADHD (s-ADHD) (n=1,495), and healthy controls (HC) (n=3,425). Data collection covered 40 variables, including demographics, routine blood counts, serum biochemical parameters, body composition and systemic inflammation markers. Analysis of Variance (ANOVA) compared differences among the three groups, and key predictors were selected via Least Absolute Shrinkage and Selection O

SUBMITTER: Meng H 

PROVIDER: S-EPMC12753890 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature