Early-Stage Detection of Autism Spectrum Using Machine Learning

Abstract

Chemical imbalance Range Problem Autism Spectrum Disorder is a neurodevelopmental issue charac- terized by challenges in friendly cooperation, correspond- ence, and tedious behaviors. Early diagnosis of ASD is crucial for effective intervention and support. This project proposes aninnovative approach to automate the detection of autism using machine learning techniquesof two differ- ent types, implemented in Google Collab. It contains four different ASD datasets representing various age groups (Toddlers, Adolescents, Children, and Adults) andinitially preprocesses the datasets. The dataset utilized for training and testing is sourced from Kaggle, providing a diverse and comprehensive set of features for robust model de- velopment. The work centers the nitty gritty component significance examination which can direct the decision- production of medical services experts while screening ASD cases.