Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by difficulties in social communication and interaction with restricted or repetitive behavior or activities. The cause of autism, in general, is unknown though genetics does play a key factor in the occurrence of the condition. In the absence of clear identifiable biomarkers, Limitations to the current diagnostic system call for the need to develop a novel method that can provide quick, cost-efficient, and accurate evaluations while maintaining a well-rounded understanding of the diverse phenotype of patients with ASD. The amelioration Machine Learning brings to automated medical diagnosis has inspired us to come up with a solution. An adept screening and diagnostic test for patients exhibiting known autistic symptoms is a well-compiled, specific, and approved questionnaire, which facilitates an easy and cheap diagnosis. Autistic Spectrum Disorder Screening Test data is collected from one such questionnaire. We used a combination of three publicly available datasets containing records related to ASD in children, adolescents, and adults. There are a total of 1100 instances along with 21 attributes. The proposed study uses a Light Gradient Boost (LGB) based model for classification, along with Random Search for hyper-parameter optimization, which yielded a high accuracy of 95.82%.
https://link.springer.com/chapter/10.1007/978-981-16-2422-3_61