Predicting autism spectrum disorder through machine learning

Since social interaction, speech, and behaviour are all impacted by autism spectrum disorder (ASD), early detection is essential for prompt intervention. Through the analysis of behavioural and demographic data, this study creates a machine learning-based model to predict ASD in children. The system effectively classifies ASD cases using the Random Forest and XGBoost algorithms, making it more accessible than conventional diagnostic techniques. Model training, feature selection, and data preprocessing are all part of the methodology, and accuracy, precision, and recall measures are used to assess performance. In order to improve early diagnosis and intervention for improved cognitive and social development, the model seeks to offer an objective, scalable, and user-friendly screening tool.