Year: 2025
Type: article
Abstract: Autism Spectrum Disorder is a neuro-developmental condition that affects multiple aspects of an individual's daily life. Early detection and intervention can significantly reduce its impact. In this paper, we propose a framework to evaluate the performance of key machine learning algorithms for early ASD detection. We apply four feature scaling techniques : Quantile Transformer , Power Transformer , Normalizer, and Max Abs Scaler , to standardize input features. These scaled datasets are then classified using four ML algorithms: AdaBoost , Random Forest , Support Vector Machine, and Linear Discriminant Analysis . The framework is tested on four publicly available ASD datasets representing different age groups: Toddlers, Children, Adolescents, and Adults. The experiments offer a detailed comparison of model performance, identifying the most effective combinations of feature scaling methods and classifiers for accurate ASD detection across age groups, enhancing the potential for improved early diagnosis
Author: Dr. G. Krishna Kishore
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: diamond
OpenAlex ID: W4408622451