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The most prevalent neurodevelopmental condition that impact in the kid stage and lasts into adulthood is ADHD. ADHD in children is often recognized and treated between the ages of 6 and 12. Although the exact origin of ADHD is unknown, it is believed that genetics, neurochemical deficits, brain network dysfunctions (such dopamine deficiency), and environmental factors all play a part. It is widely demonstrated that environmental and genetic variables, such as maternal smoking, alcohol or drug abuse, and psychological issues during pregnancy are the main causes of ADHD. Children with ADHD frequently have lower brain volumes and dysfunctional frontal and parietal cortices. This survey intends to prepare a review on different ADHD detection models adoptedin the recent 25 papers. The review includes the following (i) reviewing different ADHD detection models in terms of machine learning and deep learning strategies, (ii) Different features considered and extracted are reviewed, (iii)The datasets used in the papers arereviewed, (iv) The best performance of the modelsarereviewed, and (v) The article is concluded by identifying several research issues in the area.
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Kani et al. (2023) studied this question.
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