TY  - EJOU
AU  - Mengash, Hanan Abdullah 
AU  - Alqahtani, Hamed 
AU  - Maray, Mohammed 
AU  - Nour, Mohamed K. 
AU  - Marzouk, Radwa 
AU  - Al-Hagery, Mohammed Abdullah 
AU  - Mohsen, Heba 
AU  - Duhayyim, Mesfer Al 

TI  - Automated Autism Spectral Disorder Classification Using Optimal Machine Learning Model
T2  - Computers, Materials \& Continua

PY  - 2023
VL  - 74
IS  - 3
SN  - 1546-2226

AB  - 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 has assisted in early identify ASD by using pattern detection. Recent advances of AI models assist in the automated identification and classification of ASD, which helps to reduce the severity of the disease. This study introduces an automated ASD classification using owl search algorithm with machine learning (ASDC-OSAML) model. The proposed ASDC-OSAML model majorly focuses on the identification and classification of ASD. To attain this, the presented ASDC-OSAML model follows min-max normalization approach as a pre-processing stage. Next, the owl search algorithm (OSA)-based feature selection (OSA-FS) model is used to derive feature subsets. Then, beetle swarm antenna search (BSAS) algorithm with Iterative Dichotomiser 3 (ID3) classification method was implied for ASD detection and classification. The design of BSAS algorithm helps to determine the parameter values of the ID3 classifier. The performance analysis of the ASDC-OSAML model is performed using benchmark dataset. An extensive comparison study highlighted the supremacy of the ASDC-OSAML model over recent state of art approaches.
KW  - Autism spectral disorder; machine learning; owl search algorithm; feature selection; id3 classifier

DO  - 10.32604/cmc.2023.032729