Interpretable machine learning approaches for children's ADHD detection using clinical assessment data: an online web application deployment.

ABSTRACT:

Background

Attention-deficit/hyperactivity disorder (ADHD) is a prevalent mental disorder characterized by hyperactivity, impulsivity, and inattention. This study aims to develop a verifiable and interpretable machine learning model to identify ADHD and its subtypes in children using clinical assessment scales data.

Methods

This study utilized the ADHD-200 dataset, including demographic data, Behavioral Rating Scale, and Wechsler Intelligence Scale assessments, to train and validate our models. The model's performance was evaluated using 10-fold cross-validation within the internal dataset, and the best model will be used for external validation. Seven machine learning models were evaluated. The SHapley Additive exPlanations (SHAP) method was employed for model interpretatio

SUBMITTER: Qin H 

PROVIDER: S-EPMC11834570 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature