ADHD and ASD Classification Based on Emotion Recognition Data

Mahiye, Uluyagmur-Ozturk; Arman, Ayşe Rodopman; Yilmaz, Seval Sultan; Fındık, Onur Tuğçe Poyraz; Genç, Herdem Aslan; Gresa, Carkaxhiu-Bulut; Yazgan, M. Yanki; Teker, Umut; Çataltepe, Zehra

doi:10.1109/icmla.2016.0145

Cited by 12 publications

(2 citation statements)

References 6 publications

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“…Several ML-based classifiers were applied to predict children with ADHD [24][25][26][27][28][29]. Uluyagmur-Ozturk et al [30] conducted a study on the emotional status of children and classified them as ASD, ADHD, and control based on their diagnosis in Turkey. They extracted the data of 61 children from Maramara University Medical Hospital.…”

Section: Introductionmentioning

confidence: 99%

Predicting Children with ADHD Using Behavioral Activity: A Machine Learning Analysis

2022

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Attention deficit hyperactivity disorder (ADHD) is one of childhood’s most frequent neurobehavioral disorders. The purpose of this study is to: (i) extract the most prominent risk factors for children with ADHD; and (ii) propose a machine learning (ML)-based approach to classify children as either having ADHD or healthy. We extracted the data of 45,779 children aged 3–17 years from the 2018–2019 National Survey of Children’s Health (NSCH, 2018–2019). About 5218 (11.4%) of children were ADHD, and the rest of the children were healthy. Since the class label is highly imbalanced, we adopted a combination of oversampling and undersampling approaches to make a balanced class label. We adopted logistic regression (LR) to extract the significant factors for children with ADHD based on p-values (<0.05). Eight ML-based classifiers such as random forest (RF), Naïve Bayes (NB), decision tree (DT), XGBoost, k-nearest neighborhood (KNN), multilayer perceptron (MLP), support vector machine (SVM), and 1-dimensional convolution neural network (1D CNN) were adopted for the prediction of children with ADHD. The average age of the children with ADHD was 12.4 ± 3.4 years. Our findings showed that RF-based classifier provided the highest classification accuracy of 85.5%, sensitivity of 84.4%, specificity of 86.4%, and an AUC of 0.94. This study illustrated that LR with RF-based system could provide excellent accuracy for classifying and predicting children with ADHD. This system will be helpful for early detection and diagnosis of ADHD.

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Section: Introductionmentioning

confidence: 99%

Predicting Children with ADHD Using Behavioral Activity: A Machine Learning Analysis

2022

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“…A machine leaning techniques [62] such as Classification and Regression Trees (CART) and Chisquare Automatic Interaction Detector(CHAID) is used to classify ADHD and OSA, result shows that CART method is better computational procedure compare to CHAID. The work [63] uses emotion recognition data to classify ADHD, ASD and control group based on response time, results shows 90% of accuracy for classification. An angular velocity sensors and acceleration [64] method used to compare the hand movement of ADHD and healthy children, results are represented using radar chart, need to include more subjects for the analysis of the problem.…”

Section: Different Classification Methods For Adhdmentioning

confidence: 99%

Classification of Attention Deficit Hyperactivity Disorder (ADHD) Considering Diagnosis and Treatment

Sumathi¹,

Cholli²,

Nayak³

2019

IJMECS

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Attention Deficit Hyperactivity Disorder (ADHD) is the most frequent brain disorders in children. Brain is the greatest complicated data processing part in human body. ADHD can begin in childhood age and may extend till adolescent too. ADHD patients activities/actions/behaviour are totally different from non ADHD patients. To solve the problem in early stage is more precious contribution for children life. Otherwise the disorder may cause further destruction in child brain. An activity of ADHD child is: carelessness, impulsive, and feverish. These activities may be common in other children too but for ADHD patients these activities are more severe and more often occurs. ADHD can arise problems at school, home, it may affect children learning ability, and child may not join with others. ADHD is one among many childhood syndromes. The paper summarises the different ADHD diagnosis methods and suggested treatments for the disorder.

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Emotion Recognition in Children with Autism Spectrum Disorder Using Convolutional Neural Networks

Pavez

Diaz

Arango‐López

et al. 2021

Advances in Intelligent Systems and Computing

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ADHD and ASD Classification Based on Emotion Recognition Data

Cited by 12 publications

References 6 publications

Predicting Children with ADHD Using Behavioral Activity: A Machine Learning Analysis

Predicting Children with ADHD Using Behavioral Activity: A Machine Learning Analysis

Classification of Attention Deficit Hyperactivity Disorder (ADHD) Considering Diagnosis and Treatment

Emotion Recognition in Children with Autism Spectrum Disorder Using Convolutional Neural Networks

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