Cognitive Based Attention Deficit Hyperactivity Disorder Detection with Ability Assessment Using Auto Encoder Based Hidden Markov Model

Attention deficit hyperactivity disorder (ADHD) is a frequent Neuro-generative mental disorder. It can persist in adulthood and be expressed as cognitive complaint. Behavioural analysis of ADHD consumes more time. This multi-informant complex procedure due to the overlaps symptomatology which cause for delay diagnosis treatment. Dur these behavioural consequences various causes, no single test utilized till now diagnosing this Hence, model based on Continuous Ability Assessment Test (CAAT) enhance balance assessment. The objective behind study use deep learning with CAAT predicting ADHD. proposed Auto Encoder Based Hidden Markov Model (AE-HMM) produces low-dimensional features brain structures, novel Pearson Correlation Coefficient (PCC) employed normalizing order minimize batch effects over populations datasets. goal consistently achieved thus outperforms few standard approaches are considered like CogniLearn 3-D Convolutional Neural Networks (3DCNN). found that AE-HMM method achieves 93.68% accuracy, 90.66% sensitivity, 87.72% specificity, 87.78% F1-score 74.22% kappa score.