Distinguishing different types of attention deficit hyperactivity disorder in children using artificial neural network with clinical intelligent test

CONCLUSION: This proposed model distinguished between the ADHD-I and ADHD-C groups with 85-90% accuracy, and it distinguished between the ADHD-I, ADHD-C, and control groups with 77-86% accuracy. The machine learning model helps clinicians identify patients with ADHD in a timely manner.

via https://pubmed.ncbi.nlm.nih.gov/36710799/?utm_source=no_user_agent&utm_medium=rss&utm_campaign=None&utm_content=1lqZ3NPYysePVKsoyz66mDSgu4veDGJwnUBS47TBQPoOuNZY5J&fc=None&ff=20230216011007&v=2.17.9.post6+86293ac