Autism Spectrum Disorder Prediction Model using Machine Learning

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder that impairs social communication, behavior, speech, and nonverbal interaction. Children with ASD often exhibit distinct facial characteristics that can serve as markers for early diagnosis. This study aims to develop an automated and precise system to assist families and healthcare professionals in the early detection of ASD. By leveraging advanced deep learning techniques, specifically convolutional neural networks (CNN) and computer vision, this system analyzes facial features to differentiate between children with autism and those with typical development. The model incorporates transfer learning to enhance accuracy and efficiency in detecting autism from facial images, providing a reliable, non-invasive diagnostic tool that complements traditional screening methods. Utilizing a comprehensive video dataset, the proposed system not only detects and analyzes facial features but also focuses on emotion recognition and micro expressions. This additional layer of analysis aims to identify subtle emotional cues that may be indicative of ASD. By integrating these elements, the system establishes a dependable approach for the early prediction of ASD. This research ultimately contributes to more effective early intervention initiatives, streamlining the diagnostic process and improving outcomes for children with ASD.