An Effective Model Of Autism Spectrum Disorder Using Machine Learning

Abstract

Autism spectrum disorder (ASD) is one of the most common diseases that affect human nerves and cause a decrease in the intelligence and comprehension of the person. This disease is a group of various disorders that are characterized by poor social behavior and communication. It affects all age groups, including adults, adolescents, children, and the elderly, but the symptoms of this disease always appear in their early years. ASD suffer from problems, the most important of which are data loss, low quality, and extreme values. This makes the process of diagnosing the ASD early. Our goals in this research is to solve the ASD problems. The cussent authors proposed a technical model that solves all data problems. We used ensemble techniques that include Bayesian Boosting, Classification by Regression, Polynomial by Binominal Classification. We also used classification techniques that include CHAID, Decision Stump, Decision Tree (Weight-Based), Gradient Boosted Trees, ID3. It is proven that the proposed model solves data problems, and has obtained the highest search accuracy that has reached 100% as well as we have obtained the highest f1 measurement that has reached 100%. This proves that our work is superior to its peers.