A Comprehensive Review of Handwriting-Based ADHD Detection in Children with Autism Spectrum Disorder Using Machine Learning

By employing machine learning techniques to analyze handwriting, this study suggests a unique method of diagnosing Attention-Deficit/Hyperactivity Disorder (ADHD) in children with autism spectrum disorder (ASD). Since ASD and ADHD frequently overlap, it can be difficult to diagnose afflicted people and treat them appropriately. The goal of this project is to create an automated system that can identify signs of ADHD based on handwriting characteristics by utilizing the power of machine learning algorithms. The machine learning models are trained using a dataset of handwriting samples taken from kids with ASD and ADHD diagnoses. A range of feature extraction methods, including stroke length, pressure, and velocity, are used to extract pertinent handwriting properties. A variety of machine learning techniques are used, such as random forests, decision trees, and support vector machines (SVM).