Autism Spectrum Disorder Prediction Using Machine Learning and Design Science

Rajesh Prasad; Farida Musa; Hadiza Muhammad Ahmad; Santosh Kumar Upadhyay; Birendra Kumar Sharma

doi:10.52756/ijerr.2024.v39spl.017

Authors

Rajesh Prasad Department of Computer Science and Engineering, Ajay Kumar Garg Engineering College, Ghaziabad, UP, India https://orcid.org/0000-0002-3456-6980
Farida Musa Department of Computer Science, University of Abuja, Abuja, Nigeria https://orcid.org/0009-0001-0878-6891
Hadiza Muhammad Ahmad Department of Computer Science, University of Abuja, Nigeria https://orcid.org/0009-0005-9238-0884
Santosh Kumar Upadhyay Department of Computer Science and Engineering, Ajay Kumar Garg Engineering College, Ghaziabad, UP, India https://orcid.org/0000-0003-0450-2051
Birendra Kumar Sharma Department of MCA, Ajay Kumar Garg Engineering College, Ghaziabad, U.P., India https://orcid.org/0000-0002-5526-5263

DOI:

https://doi.org/10.52756/ijerr.2024.v39spl.017

Keywords:

Autism Spectrum Disorder, Machine Learning, Random Forest, Principal components analysis, Feature selections

Abstract

Machine learning, a subset of Artificial Intelligence, has gained much recognition in facilitating disease prediction and the decision-making process in healthcare. One of the most often diagnosed developmental disorders in the world is Autism Spectrum Disorder (ASD). Around the world, it is reported to afflict 75 million people and the number of cases has gradually increased since studies began in the 1960s. The symptoms generally include communication deficits, sensory processing differences, and repetitive actions or behaviors. This research develops a model to detect ASD using Principal Component Analysis and Machine Learning algorithms to classify and predict the risk of ASD among pregnant women. Data was collected from National Hospital in Abuja, Nigeria. According to the results, PCA and Random Forest produced the best accuracy of 98.7%. Bayesian probability was employed to evaluate and verify the reliability of the model. The created model can aid doctors in diagnosing ASD.