Early detection of autism spectrum disorder based on parental input

Nowadays, the Autism Spectrum Disorder (ASD) movement is moving at a breakneck pace. Screening for autism characteristics is a laborious and costly process. It is now possible to detect autism in its early stages thanks to developments in AI and ML. Although several research have been conducted using various methods, no conclusive results have been drawn regarding the prediction of autism features by age group.

Consequently, the purpose of this paper is to establish a mobile app that may predict ASD in individuals of any age using a model that is based on ML techniques. The study's results include a mobile app built on top of a prediction model for autism that was created by combining Random Forest- and Adaboost. The AQ-10 dataset and 250 real datasets, gathered from both autistic and non-autistic individuals, were used to assess the suggested model. In terms of accuracy, specificity, sensitivity, precision, and false positive rate (FPR), the evaluation findings demonstrated that the suggested prediction model produced superior outcomes for both types of datasets.