Investigating multiclass autism spectrum disorder classification using machine learning techniques

• DOI: 10.1016/j.prime.2024.100602
• Publication Date: 2024-05-18T04:40:42Z
• Authors: Puneet Bawa; Virender Kadyan; Archana Mantri; Harsh Vardhan
• Keywords: Multiclass classification

The diagnosis and classification of autism spectrum disorder (ASD) presents anatomical difficulty owing to the existence a wide range symptoms that may be organized into many categories. present research investigates efficacy machine learning methods for facilitating recognition individuals who have been diagnosed with ASD. primary aim this study has assess effectiveness multiple algorithms based on in identifying intricate patterns seen datasets related ASD, which includes diagnostic results indicate Logistic Regression approach demonstrated great levels accuracy, rates 94.3% children 99% adolescents binary system. Similarly, it reported Support Vector Machine (SVM) had superior performance compared all other systems test focused adults exclusively, an accuracy rate 98.5%. Moreover, supplementary series experiments conducted combined dataset children, adolescents, resulted observation SVM exhibited notable 97.2% 99.55% multiclass classification, encompassing from diverse age groups. provide evidence favor progress achieved treatment ASD as result capacity detect categorize at earlier developmental phase.