Predictive analytics using a Machine Learning Model to recommend the most suitable Intervention Technology for Autism related deficits

Abstract

This body of work aims to collect and analyse data from previous studies completed on technological interventions (to aid autism related deficits such as social behaviour, communication and limited interest and actions that are both distinct and repetitive) [1] for people with Autism and a build machine learning model for predicting the most suitable intervention technology for a single or combination of deficits related to Autism. The author selected and collected all relevant data from current available studies. This data was used to build and train supervised classification machine learning model to predict the most suitable intervention technology for a single or combination of deficits related to autism based on deficits presented by an individual. Results indicated that machine learning is an effective tool for building a predictive model to recommend the most effective intervention technology for Autism related deficits based on data integrated from the studies. The outcomes have implications for medical professionals, caregivers, teachers and family members in effectively selecting technological intervention for autism related deficits. These interventions could help the individual better cope with their disability and potentially lessen its impact.