Attention Deficit Hyperactivity Disorder (ADHD) is a disorder that starts in childhood, sometimes persisting into adulthood. It puts a strain on their social, professional, family, and environmental lives, which can exacerbate disorders such as anxiety, depression, and bipolar disorder. Background/Objectives: This paper aims to predict ADHD in children and adults and explain the main factors impacting this disorder. Methods: We start by introducing the main symptoms and challenges ADHD poses for children and adults such as epilepsy and depression. Then, we present the results of existing research on three ADHD comorbidities: anxiety, depression, and bipolar disorder, and their possible continuity in adulthood with therapeutic implications. After that, we explain the impact of this disorder and its relationship with these comorbidities on the affected patient’s health and environment and list proposed treatments. We propose a methodology for predicting this impairment in children and adults by using Machine Learning algorithms (ML), Explainable Artificial Intelligence (XAI), and two datasets, the National Survey for Children’s Health (NSCH) (2022) for the children and the ADHD|Mental Health for the adults. Results: Logistic Regression (LR) was the most suitable algorithm for children, with an accuracy of 99%. As for adults, the XGBoost (XGB) was the most performant ML method, with an accuracy of 100%. Conclusions: Lack of sleep and excessive smiling/laughing are among the factors having an impact on ADHD for children, whereas anxiety and depression affect ADHD adults.

Background: A child with Attention-Deficit Hyperactivity Disorder (ADHD) faces two issues: inattention and hyperactivity/impulsivity. These two symptoms make the child’s life more challenging compared to non-ADHD individuals. Therefore, one of the steps toward better quality of life involves cooperation with and contact with the environment to better address this condition. Thanks to Artificial Intelligence (AI), doctors, caregivers, and parents are increasingly better able to understand the hardships these children face. One AI technique is Reinforcement Learning (RL). Methods: We propose an RL model simulation with 44 child agents with or without ADHD, using the Independent Deep Q Network (IDQN), Value Decomposition Network (VDN), and QMIX algorithms. Results: Comparing the results obtained with these three algorithms, children with ADHD find it more challenging to choose the maximum rewards than neurotypical children (395 at episode 300 for non-ADHD compared to 340 at episode 120 for ADHD using IDQN, 69 from episode 90 for ADHD compared to 82 for non-ADHD via VDN, and 31 at episode 110 for ADHD versus 28 at episode 110 for non-ADHD with QMIX). Conclusions: The simulated ADHD agents struggle to aim for the maximum rewards as much as neurotypical children.