Prediction of Attention Deficit Hyperactivity Disorder Using Machine Learning Models

Machine learning models are increasingly utilized to predict ADHD based on various features and behavioural indicators. These models analyze demographic, clinical, and behavioural data to distinguish individuals with ADHD from those without, showing promising results in accurate prediction. Feature selection techniques are applied to identify key factors influencing predictions, and enhancing diagnostic procedures for ADHD. This paper showcases the promising application of machine learning for early ADHD diagnosis and predicting treatment outcomes, thereby enhancing diagnostic tools’ efficiency. By employing machine learning to characterize ADHD, valuable insights into its neural correlates and underlying mechanisms are gained, facilitating a deeper understanding of its complexity and variability. Overall, machine learning shows significant promise in improving ADHD diagnosis and intervention strategies.