Automated Autism Spectral Disorder Classification Using Optimal Machine Learning Model

Autism Spectrum Disorder (ASD) refers to a neuro-disorder where an individual has long-lasting effects on communication and interaction with others. Advanced information technology which employs artificial intelligence (AI) model assisted in early identify ASD by using pattern detection. Recent advances of AI models assist the automated identification classification ASD, helps reduce severity disease. This study introduces owl search algorithm machine learning (ASDC-OSAML) model. The proposed ASDC-OSAML majorly focuses ASD. To attain this, presented follows min-max normalization approach as pre-processing stage. Next, (OSA)-based feature selection (OSA-FS) is used derive subsets. Then, beetle swarm antenna (BSAS) Iterative Dichotomiser 3 (ID3) method was implied for detection classification. design BSAS determine parameter values ID3 classifier. performance analysis performed benchmark dataset. An extensive comparison highlighted supremacy over recent state art approaches.