AUTISM PREDICTION USING MACHINE LEARNING

Autism Spectrum Disorder (ASD) is a complex neurological condition that affects social interaction, communication, and behavioral patterns. Early diagnosis and intervention are critical to improving the quality of life for individuals with autism. Traditional diagnostic methods often rely on time-consuming behavioral assessments conducted by specialists, which can delay detection and intervention. This study proposes a machine learning- based approach to predict autism by analyzing behavioral, demographic, and clinical data. By leveraging algorithms such as Random Forest, Support Vector Machines (SVM), and Neural Networks, the system identifies patterns and correlations in data that are indicative of autism traits. Features such as social interaction behaviors, communication responses, and demographic factors are used to train and validate the predictive model. The proposed system aims to provide a faster, cost-effective, and scalable solution for autism screening, enabling early detection and reducing the burden on healthcare professionals. This machine learning framework not only enhances diagnostic accuracy but also facilitates personalized interventions, contributing to better outcomes for individuals and their families. Keywords: Autism Spectrum Disorder (ASD), Machine Learning, Jupyter Notebook, Python, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Logistic Regression, XGBoost, Voting Classifier, Early Diagnosis, Predictive Modeling, Feature Selection, Data Classification, Autism Screening, Healthcare AI, Behavioral Data Analysis, Clinical Data Processing, Supervised Learning, Ensemble Learning.