Abstract

Autism spectrum disorder (ASD) is challenging to diagnose and treat in its early stages due to its lengthy and costly diagnostic procedures. This study investigates how machine learning (ML) methods like SVM, NB, LR, and KNN may be used to develop effective ASD screening systems that will help people and healthcare providers alike. Additionally, new methods for fall detection and ASD screening are introduced by the combination of ML and Internet of Things (IoT) technology. This study provides a holistic approach to healthcare by tracking motion with the help of IoT devices. The system's capacity to promptly notify caregivers in the event of a fall demonstrates how well it may be used to improve safety and prompt response for people with ASD.