AutiLife: A Healthcare Monitoring System for Autism Center in 5G Cellular Network using Machine Learning Approach Autism is a complex and developmental neurobehavioral disorder which indicates difficulties with communication skills and social interactions. Because of various types of symptoms, this condition is also refers to as autism spectrum disorder (ASD). There are many autism centers to help and facilitate children with autism. To make an autism center automatic and capable of responsive in real time, the next generation of cellular network, i.e. 5G can play a vital role. We have found minor contribution towards healthcare monitoring system for autism centers in a network that offers ultrareliable and lowlatency communication (uRLLC), higher data rates and massive connectivity of devices in Internet of Things (IoT) and Internet of Medical things (IoMT). Therefore, we have proposed AutiLife an impeccable healthcare monitoring system for autism centers in 5G cellular network using Machine Learning algorithm, Support Vector Machine (SVM). Our proposed system model will collect health related data (Blood Pressure, Heart Rate, Body Temperature, Body Motion, Speech Signals) using various sensors and devices from autistic children. Then using ML algorithm the system will accomplish some course of actions and alert the controllers and nearby hospitals if any health falling issues are found. We resolutely believe, our proposed system AutiLife may handle any emergency issues for example epilepsy, heart stroke, heart attack, anxiety, hysteria that can occur on any sudden moment in an autism center and may save inestimable life of children with autism.

An Approach for Severity Prediction of Autism Using Machine Learning Effective early diagnosis of autism can have a significant impact on its intervention and treatment. In this paper, an approach is proposed for comprehensively considering genetic factors and environmental factors to predict the severity of autism. According to the Childhood Autism Rating Scale (CARS), a sample set was collected from the autism clinic and a predictive model based on a stacked sparse autoencoder combined with a softmax classifier was constructed. We compared the proposed model with decision trees and support vector machines. Experiments show that the proposed model has a highest accuracy in predicting the severity of autism. Our method can help patients predict their condition and assist doctors in accurate diagnosis.