Machine learning approach for classification of ADHD adults

Tenev, Aleksandar; Markovska-Simoska, Silvana; Kocarev, Ljupčo; Pop-Jordanov, Jordan; Müller, Andreas; Candrian, Gian

doi:10.1016/j.ijpsycho.2013.01.008

Cited by 144 publications

(88 citation statements)

References 21 publications

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“…The comparatively higher resolution (usually one cubic millimeter or less) of structural MRI data (Arbabshirani et al, 2017) makes it a better choice for the use in classification experiments either as a standalone or as a co-modality in multi-modal experimental settings. The most abundantly used and popular machine learning tool among the neuroimaging community is support vector machine (SVM) (Tenev et al, 2014; Arbabshirani et al, 2017). In this comparative study, we compared an extreme learning machine (ELM) based classification framework with SVMs.…”

Section: Introductionmentioning

confidence: 99%

Multi-modal, Multi-measure, and Multi-class Discrimination of ADHD with Hierarchical Feature Extraction and Extreme Learning Machine Using Structural and Functional Brain MRI

Qureshi

Min

et al. 2017

Front. Hum. Neurosci.

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Structural and functional MRI unveil many hidden properties of the human brain. We performed this multi-class classification study on selected subjects from the publically available attention deficit hyperactivity disorder ADHD-200 dataset of patients and healthy children. The dataset has three groups, namely, ADHD inattentive, ADHD combined, and typically developing. We calculated the global averaged functional connectivity maps across the whole cortex to extract anatomical atlas parcellation based features from the resting-state fMRI (rs-fMRI) data and cortical parcellation based features from the structural MRI (sMRI) data. In addition, the preprocessed image volumes from both of these modalities followed an ANOVA analysis separately using all the voxels. This study utilized the average measure from the most significant regions acquired from ANOVA as features for classification in addition to the multi-modal and multi-measure features of structural and functional MRI data. We extracted most discriminative features by hierarchical sparse feature elimination and selection algorithm. These features include cortical thickness, image intensity, volume, cortical thickness standard deviation, surface area, and ANOVA based features respectively. An extreme learning machine performed both the binary and multi-class classifications in comparison with support vector machines. This article reports prediction accuracy of both unimodal and multi-modal features from test data. We achieved 76.190% (p < 0.0001) classification accuracy in multi-class settings as well as 92.857% (p < 0.0001) classification accuracy in binary settings. In addition, we found ANOVA-based significant regions of the brain that also play a vital role in the classification of ADHD. Thus, from a clinical perspective, this multi-modal group analysis approach with multi-measure features may improve the accuracy of the ADHD differential diagnosis.

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Section: Introductionmentioning

confidence: 99%

Multi-modal, Multi-measure, and Multi-class Discrimination of ADHD with Hierarchical Feature Extraction and Extreme Learning Machine Using Structural and Functional Brain MRI

Qureshi

Min

et al. 2017

Front. Hum. Neurosci.

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“…Tenev et al applied SVM to classify ADHD patients from control group [7]. Four SVM classifiers were trained with different data set, taken for four different conditions, and then the output of the classifiers is logically combined.…”

Section: Introductionmentioning

confidence: 99%

“…The remaining paper is organized as follows. Section 2 clarifies technical details of how to obtain classification models with optimal hyper-ISBN 978-93-84422-76-9 6th International Conference on Developments in Engineering and Technology (ICDET-2017) Bangkok (Thailand) Feb. [6][7]2017 parameters to the problem of identification of childhood disabilities. In Section 3, results and discussions are presented on exhaustive comparison of performance measures of various classification models.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approach to Classification of Childhood Disabilities for Special Education

2017

ICDET-17, FABMS-17, RIECAE-17, RTET-17, ABMS-17 Feb. 2017

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“…In previous work, some models either use hundreds of features as an input or exhaustively search on a preselected smaller subset of features. SVM is mostly favored [7] and some variant of feedforward neural networks [8] is also used. We believe that those methods are either susceptible to overfitting or too restrictive in the search space.…”

Section: Introductionmentioning

confidence: 99%

An integrated feature ranking and selection framework for ADHD characterization

et al. 2016

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Today, diagnosis of attention deficit hyperactivity disorder (ADHD) still primarily relies on a series of subjective evaluations that highly rely on a doctor’s experiences and intuitions from diagnostic interviews and observed behavior measures. An accurate and objective diagnosis of ADHD is still a challenge and leaves much to be desired. Many children and adults are inappropriately labeled with ADHD conditions, whereas many are left undiagnosed and untreated. Recent advances in neuroimaging studies have enabled us to search for both structural (e.g., cortical thickness, brain volume) and functional (functional connectivity) abnormalities that can potentially be used as new biomarkers of ADHD. However, structural and functional characteristics of neuroimaging data, especially magnetic resonance imaging (MRI), usually generate a large number of features. With a limited sample size, traditional machine learning techniques can be problematic to discover the true characteristic features of ADHD due to the significant issues of overfitting, computational burden, and interpretability of the model. There is an urgent need of efficient approaches to identify meaningful discriminative variables from a higher dimensional feature space when sample size is small compared with the number of features. To tackle this problem, this paper proposes a novel integrated feature ranking and selection framework that utilizes normalized brain cortical thickness features extracted from MRI data to discriminate ADHD subjects against healthy controls. The proposed framework combines information theoretic criteria and the least absolute shrinkage and selection operator (Lasso) method into a two-step feature selection process which is capable of selecting a sparse model while preserving the most informative features. The experimental results showed that the proposed framework generated the highest/comparable ADHD prediction accuracy compared with the state-of-the-art feature selection approaches with minimum number of features in the final model. The selected regions of interest in our model were consistent with recent brain–behavior studies of ADHD development, and thus confirmed the validity of the selected features by the proposed approach.

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Machine learning approach for classification of ADHD adults

Cited by 144 publications

References 21 publications

Multi-modal, Multi-measure, and Multi-class Discrimination of ADHD with Hierarchical Feature Extraction and Extreme Learning Machine Using Structural and Functional Brain MRI

Multi-modal, Multi-measure, and Multi-class Discrimination of ADHD with Hierarchical Feature Extraction and Extreme Learning Machine Using Structural and Functional Brain MRI

Machine Learning Approach to Classification of Childhood Disabilities for Special Education

An integrated feature ranking and selection framework for ADHD characterization

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