Autism Detection: Leveraging Machine Learning for Objective and Comprehensive Assessment from Behavioral and Physiological Data

Early diagnosis of autism spectrum disorder (ASD) presents a significant challenge because it currently depends on lengthy behavioral assessments conducted by subjective evaluation methods. The proposed method uses machine learning techniques to detect ASD through the analysis of behavioral indicators together with physiological data provided with early and objective and complete evaluation methods. The presented system detects developmental anomalies along with standard benchmarks through automated data acquisition tools along with advanced algorithms along with video analysis and wearable sensors. Explaining AI technology through XAI enables doctors and caregivers to understand how the system makes choices while building their trust in its operations. The system improves diagnostic precision by minimizing human judgment while helping healthcare providers deliver timely interventions for ASD patients to raise care quality.