Autism Spectrum Disorder Detection

Early detection of Autism Spectrum Disorder (ASD) is crucial for timely intervention and improved outcomes. This research leverages machine learning techniques to analyze growth patterns and behavioral indicators for early ASD identification.

Research Methodology

Our approach combines multiple data sources including developmental milestones, behavioral assessments, and growth metrics to create comprehensive predictive models.

Key Components

• Behavioral pattern analysis
• Growth trajectory modeling
• Machine learning classification algorithms
• Early intervention recommendations

Expected Impact

This research aims to provide healthcare professionals with reliable tools for early ASD screening, potentially improving diagnosis accuracy and enabling earlier therapeutic interventions.