ArticlePloS one2013
Extreme learning machine-based classification of ADHD using brain structural MRI data.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers, 3 of them syntheses that pooled it.
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Who cites it
61 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- Towards a brain-based predictome of mental illness.Human brain mapping · 2020Pooled it
- Multivariate analyses applied to fetal, neonatal and pediatric MRI of neurodevelopmental disorders.NeuroImage. Clinical · 2015Pooled it
- Trial
- Identifying individuals with attention deficit hyperactivity disorder based on temporal variability of dynamic functional connectivity.Scientific reports · 2018Trial
- Trial
- Q-MIND: enhancing ADHD diagnosis using quantum machine learning for advanced neuroimaging analysis.Journal of neural transmission (Vienna, Austria : 1996) · 2025Article
- Integrating explainable AI with clinical features to enhance ADHD diagnostic understanding.Frontiers in psychiatry · 2025Article
- Current Perspectives on Attention-deficit Hyperactivity Disorder.Current molecular medicine · 2025Review
- Comparison of the performance of machine learning algorithms for the task-switching functional magnetic resonance imaging data for distinguishing attention deficit hyperactivity disorder from bipolar disorder.Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences · 2025Article
- Artificial intelligence for children with attention deficit/hyperactivity disorder: a scoping review.Experimental biology and medicine (Maywood, N.J.) · 2025Article
- Unlocking the potential of wearable technology: Fitbit-derived measures for predicting ADHD in adolescents.Frontiers in child and adolescent psychiatry · 2025Article
- Machine Learning Techniques to Predict Mental Health Diagnoses: A Systematic Literature Review.Clinical practice and epidemiology in mental health : CP & EMH · 2024Review
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Machine learning in attention-deficit/hyperactivity disorder: new approaches toward understanding the neural mechanisms.Translational psychiatry · 2023Review
- Morphological brain alterations in dialysis- and non-dialysis-dependent patients with chronic kidney disease.Metabolic brain disease · 2023Article
- Predicting childhood and adolescent attention-deficit/hyperactivity disorder onset: a nationwide deep learning approach.Molecular psychiatry · 2023Article
- A Review of Machine Learning and Deep Learning Approaches on Mental Health Diagnosis.Healthcare (Basel, Switzerland) · 2023Review
- Structural and diffusion-weighted brain imaging predictors of attention-deficit/hyperactivity disorder and its symptomology in very young (4- to 7-year-old) children.The European journal of neuroscience · 2022Article
- Altered neural flexibility in children with attention-deficit/hyperactivity disorder.Molecular psychiatry · 2022Article
1 more citing papers are in PubMed but not listed here.
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4 authors.
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Abstract
backgroundEffective and accurate diagnosis of attention-deficit/hyperactivity disorder (ADHD) is currently of significant interest. ADHD has been associated with multiple cortical features from structural MRI data. However, most existing learning algorithms for ADHD identification contain obvious defects, such as time-consuming training, parameters selection, etc. The aims of this study were as follows: (1) Propose an ADHD classification model using the extreme learning machine (ELM) algorithm for automatic, efficient and objective clinical ADHD diagnosis. (2) Assess the computational efficiency and the effect of sample size on both ELM and support vector machine (SVM) methods and analyze which brain segments are involved in ADHD.
methodsHigh-resolution three-dimensional MR images were acquired from 55 ADHD subjects and 55 healthy controls. Multiple brain measures (cortical thickness, etc.) were calculated using a fully automated procedure in the FreeSurfer software package. In total, 340 cortical features were automatically extracted from 68 brain segments with 5 basic cortical features. F-score and SFS methods were adopted to select the optimal features for ADHD classification. Both ELM and SVM were evaluated for classification accuracy using leave-one-out cross-validation.
resultsWe achieved ADHD prediction accuracies of 90.18% for ELM using eleven combined features, 84.73% for SVM-Linear and 86.55% for SVM-RBF. Our results show that ELM has better computational efficiency and is more robust as sample size changes than is SVM for ADHD classification. The most pronounced differences between ADHD and healthy subjects were observed in the frontal lobe, temporal lobe, occipital lobe and insular.
conclusionOur ELM-based algorithm for ADHD diagnosis performs considerably better than the traditional SVM algorithm. This result suggests that ELM may be used for the clinical diagnosis of ADHD and the investigation of different brain diseases.
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