Machine Learning Based Early Detection of Autism Spectrum Disorder

Abstract

The Autism Screening System is a web-based application designed to support early detection of Autism Spectrum Disorder (ASD) using machine learning techniques. Early identification is important for timely intervention and improving individuals' quality of life. The system is developed using the Flask framework and provides an interactive interface for healthcare professionals. It analyzes various clinical parameters such as age, gender, family history, communication skills, social interaction, and behavioral patterns. Multiple machine learning algorithms, including Random Forest, Decision Tree, K-Nearest Neighbors, Naive Bayes, and Logistic Regression, are implemented and compared. The model with the highest accuracy is automatically selected for prediction. The system also includes graphical visualization to evaluate model performance. It generates predictions along with confidence levels to assist doctors in decision-making. This tool is intended to support, not replace, clinical diagnosis. Overall, it provides a fast and data-driven approach for preliminary ASD screening, with potential for future improvements using real-world data and healthcare deployment