• Sanju S Anand
• Dr. Shashidhar Kini

Purpose: Autism Spectrum Disorder (ASD) is a complicated neurological syndrome characterized by difficulties with social interaction and communication, as well as restricted and repetitive behaviors. Early detection and intervention are critical for improving outcomes for people with ASD. Advanced machine learning approaches have showed promise in assisting with the study and identification of ASD in recent years. This study provides an overview of how advanced machine learning techniques can be used to analyze and detect ASD. This study investigates multiple data sources utilized for ASD analysis, including behavioral data, brain imaging data, genetic data, and electronic health records. It addresses how machine learning algorithms might extract significant patterns and features from these disparate datasets to aid in ASD diagnosis and categorization.

Autism Spectrum Disorder (ASD), Classification, Feature extraction, Machine learning algorithms, Support Vector Machine.

"Analysis and Detection of Autism Spectrum Disorder Using Advanced Machine Learning Techniques: A Review of Literature.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 12, page no.e333-e354, December-2023, Available :http://www.jetir.org/papers/JETIR2312443.pdf

"Analysis and Detection of Autism Spectrum Disorder Using Advanced Machine Learning Techniques: A Review of Literature.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 12, page no. ppe333-e354, December-2023, Available at : http://www.jetir.org/papers/JETIR2312443.pdf