Study of prevalence and factors affecting prevalence of attention deficit hyperactivity disorder in primary school children

Singanamala, Harish; Kumar, Parveen; Sridhar, P; Thammanna, P S

doi:10.18203/2349-3291.ijcp20211420

Cited by 2 publications

(1 citation statement)

References 23 publications

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“…Many relevant studies [17][18] to classify ADHD rely on electroencephalography (EEG), fMRI, and eye movement data. The high incidence of ADHD in children with clinical symptoms will continue into adulthood by showing destructive elements due to the lack of appropriate treatments [19].…”

Section: Introductionmentioning

confidence: 99%

Detection Attention Deficit Hyperactivity Disorder by using Convolution Neural Network

salah,

Shokair,

Abd El-Samie

et al. 2023

International Journal of Telecommunications

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Attention deficit hyperactivity disorder (ADHD) is a neurological disease that is very common in recent times, and many attempts have been made to overcome it. ADHD is diagnosed in boys more than girls. Girls are more likely to have only symptoms of inattention, and less likely to exhibit disruptive behavior that makes ADHD symptoms more noticeable. This means that girls with ADHD may not always be diagnosed. Artificial intelligence has played a very important role in eliminating this disorder using deep learning technology. Deep learning has three algorithms as Deep Neural Network (DNN), convolution neural network (CNN), Recurrent Neural Network (RNN). The disease is diagnosed using functional magnetic resonance imaging (fMRI) to determine whether the person is affected or not by taking some snapshots of brain images. A convolutional neural network (CNN) was chosen to extract the specifications or features of fMRI images.There were an optimization technique of the fMRI datasets namely, Nesterov-Accelerated Adaptive Moment Estimation (Nadam). Using these optimization techniques for adapting the classification system for three CNN network or models for ADHD cases, it was concluded that the accuracy for CNN NET 1 is 97.5%, accuracy for CNN NET 2 is 95% and accuracy for CNN NET 3 is 98.75 %. Finally, it's found that CNN NET 3 is the best as its high accuracy so the system is improved.

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Section: Introductionmentioning

confidence: 99%

Detection Attention Deficit Hyperactivity Disorder by using Convolution Neural Network

salah,

Shokair,

Abd El-Samie

et al. 2023

International Journal of Telecommunications

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Effectiveness of A Video Awareness Programme regarding Attention Deficit Hyperactivity Disorder among Primary School Teachers and Parents of selected schools in Guntur (Dt), Andhra Pradesh

Venkateswarlu

Jyothi

Suneetha

2022

IJANM

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Background: Attention-Deficit Hyperactivity Disorder (ADHD) is one of the popular neuro developmental disorder among kids, adolescents and in adults. The main Objective of the study isto assess the effectiveness of a video awareness programme regarding Attention deficit hyperactivity disorder (ADHD) among primary school teachers and parents of primary school children.Materials and Methods: A pre experimental, one group pre-test and post-test research design was adopted for this study. The study was conducted in selected schools of Mangalagiri, Guntur district, Andhra Pradesh. Fifty subjects of primary school teachers and sixty subjects of parents were selected by using convenient sampling technique. A structured questionnaire was developed to collect the sociodemographic data and Knowledge on Attention Deficit Disorders scale (KADDS) was used to assess the effectiveness of Video awareness programme regarding ADHD. Results: The findings of the study shown that Majority (62%) of the primary school teachers had bachelor’s degree in elementary education. Where as in parents’group majority (53%) of the parents had high school certificate. In primary school teachers, the mean score of pre-test was 16.78±3.78, and post-test mean score was 31.5±2.74, the obtained ‘t’ value (t=20.84, df = 49). In parents of primary school children group,the pre-test mean score was 15.06±3.91 and post-test mean score was 28.11±4.35, the obtained ‘t’ value (t=17.64, df= 59). The chi-square shown that there was a significant association between the primary school teachers pre-test level of knowledge with their (χ2=6.04) age, (χ2=8.7) education level,in parents of primary school children, there was a significant association between pre-test level of knowledge with their education level (χ2=19.64), family income (χ2=5.49). Conclusion:The study concluded that the level of knowledge was improved after administration of Video. Thus, Video is an effective strategy which can help in improving the level of knowledge among primary school teachers and parents of primary school children.

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Study of prevalence and factors affecting prevalence of attention deficit hyperactivity disorder in primary school children

Cited by 2 publications

References 23 publications

Detection Attention Deficit Hyperactivity Disorder by using Convolution Neural Network

Detection Attention Deficit Hyperactivity Disorder by using Convolution Neural Network

Effectiveness of A Video Awareness Programme regarding Attention Deficit Hyperactivity Disorder among Primary School Teachers and Parents of selected schools in Guntur (Dt), Andhra Pradesh

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