Machine Learning Methods to Detect Autism Among Children

Autism Spectrum Disorder (ASD) is a developmental condition affecting communication, behavior, and social interaction. Early detection is crucial for timely intervention, yet it is often delayed due to limited specialists and difficulty in recognizing symptoms. In this study, we propose a machine learning-based approach using Natural Language Processing (NLP) and PySpark to analyze unstructured text data from online forums, social media, and caregiver reports. Our methodology involves data collection, feature selection, and classification using deep learning models such as LSTM-RNN. By leveraging PySpark’s scalability, we process large text datasets efficiently to identify linguistic markers of ASD. The goal is to enhance early autism detection by analyzing caregiver-reported observations, ultimately supporting early intervention efforts. Future research will explore advanced ML techniques to reduce overfitting and improve model performance. This study contributes to ASD research by demonstrating the potential of NLP-driven approaches for scalable and automated autism detection