© 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 710 NEUROASSIST ADHD ANALYZER: A SMART APPLICATION FOR RECOGNIZING ATTENTION DEFICIT HYPERACTIVITY DISORDER (ADHD) LEVELS IN CHILDREN Gamage L.S. Department of Information Technology Sri Lanka Institute of Information Technology Sri Lanka it19382036@my.sliit.lk Shehan R.H.A. Department of Information Technology Sri Lanka Institute of Information Technology Sri Lanka it19987576@my.sliit.lk Bambarandage T. M. L. Department of Information Technology Sri Lanka Institute of Information Technology Sri Lanka it19176116@my.sliit.lk Anjali W.M.S. Department of Information Technology Sri Lanka Institute of Information Technology Sri Lanka it19989242@my.sliit.lk Thelijjagoda S. Department of Information Technology Sri Lanka Institute of Information Technology Sri Lanka samantha.t@sliit.lk Krishara J. Department of Information Technology Sri Lanka Institute of Information Technology Sri Lanka jenny.k@sliit.lk ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Attention Deficit Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder that affects children. Identifying children with ADHD is a challenging task, as it requires specialized training and expertise. A novel approach to identifying children with ADHD using artificial neural networks (ANNs) and convolutional neural networks (CNNs) was proposed in this research paper. The web application "NEUROASSIST ADHD Analyzer" was developed to assist in the identification of children with attention deficit hyperactivity disorder (ADHD) using electroencephalogram (EEG) and facial expression data. The application includes three focus games, focus activities, a facial expression analyzer, a combined probability predictor, and a BOT application for the SNAP IV questionnaire. The ANN and CNN models were used to implement these components. An LR model was used to predict the overall result. The integration of these multiple models into the combined model resulted in an accuracy of 94%, with a sensitivity of 90% and a specificity of 92%. Additionally, the area under the receiver operating characteristic curve (AUC- ROC) was 0.91, indicating good discriminative power. Furthermore, a user study indicated that the application is user-friendly and effective in assisting with the diagnosis of ADHD. This research demonstrates the potential of ANNs and CNNs in identifying children with ADHD and presents a promising tool for assisting clinicians in diagnosing the disorder. Future work could focus on improving the accuracy of the models by incorporating additional data sources and features and expanding the application to a larger population. Key Words: ADHD, EEG, CNN, ANN, machine learning 1. INTRODUCTION ADHD or attention deficit hyperactivity disorder is a condition that affects both children and adults and is categorized as a neurodevelopmental disorder. The disorder is characterized by a continuous pattern of inattention, hyperactivity, and impulsivity that can significantly hinder daily functioning and development. Inattention symptoms include difficulties sustaining attention, making careless mistakes, organizing tasks and activities, and following through on instructions. Fidgeting, excessive chatting, interrupting others, and difficulty waiting for one's turn are only a few characteristics of hyperactivity-impulsivity [1]. ADHD is an important topic to research due to its high prevalence and the detrimental effects it may have on people as well as society at large. The National Institute of Mental Health (NIMH) estimates that 9.4% of American children between the ages of 2 and 17 have ADHD. The disorder can persist into adulthood, with approximately 4.4% of US adults estimated to have ADHD [2]. ADHD can lead to significant impairments in various areas of functioning, including academic and occupational achievement, social skills, and mental health. Individuals with ADHD are at an increased risk of [3] experiencing problems such as academic underachievement, relationship difficulties, and substance abuse. Moreover, ADHD can cause a significant financial burden, as individuals with the disorder often require more medical care and may have decreased earning potential [4]. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 06 | Jun 2023 www.irjet.net p-ISSN: 2395-0072