Early-Stage Autism Spectrum Disorder Diagnosis Using Machine Learning

Abstract: The project shows a way to use Machine Learning (ML) to find Autism Spectrum Disorder (ASD) early on, acknowledging the challenges of diagnosing the condition while striving to mitigate its severity through early interventions. The suggested system uses four typical ASD datasets, ranging from infants to adults, to test four Feature Scaling (FS) techniques: Quantile Transformer, Power Transformer, Normalizer, and Max Abs Scaler. Included scaled datasets are used for machine learning computations (like K-Nearest Neighbors, Gaussian Naïve Bayes, Logistic Regression, SVM, LDA, Ada Boost, and Random Forest). Factual estimations used to Find the best FS methods and classifiers for each age group. Babies, children, adolescents, and adults are the groups for which the voting classifier most accurately predicts ASD. The assignment includes an analysis of the relevance of a specific aspect. Employing four Component Determination Strategies to help medical care professionals with ASD screening and to emphasize the importance of calibrating machine learning approaches in predicting ASD across age groups. The suggested structure outperforms the existing early ASD finding methods. A group process that used a Voting Classifier with Random Forest (RF) and AdaBoost was able to get 100% accuracy, which made ASD recognition even stronger and more accurate.

Keywords: Machine Learning, Classification, Autism Spectrum Disorder, Feature Scaling, and Feature Selection Methods.