Abstract
Autism Spectrum Disorder (ASD) is a disorder which takes place in the developmental stages of an individual and affects the language learning, speech, cognitive, and social skills, and impacts around 1% of the population globally [14]. Even though some individuals are diagnosed with ASD, they can portray outstanding scholastic, non-academic, and artistic capabilities, which thus proves to be challenging to the scientists trying to provide answers to this. At present, standardized tests are the only methods which are used clinically, in order to diagnose ASD. This not only requires prolonged diagnostic time but also faces a steep increase in medical costs. In recent years, scientists have tried to investigate ASD by using advanced technologies like machine learning to improve the precision and time required for diagnosis, as well as the quality of the whole process. Models such as Support Vector Machines (SVM), Random Forest Classifier (RFC), Naïve Bayes (NB), Logistic Regression (LR) and KNN have been applied to our dataset and predictive models have been constructed based on the outcome. Our objective is to thus determine if the child is susceptible to neurological disorders such as ASD in its nascent stages, which would help streamline the diagnosis process.
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Vakadkar, K., Purkayastha, D., Krishnan, D. (2020). Early Detection of Autism Spectrum Disorder in Children Using Supervised Machine Learning. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T., Valentino, G. (eds) Advances in Computing and Data Sciences. ICACDS 2020. Communications in Computer and Information Science, vol 1244. Springer, Singapore. https://doi.org/10.1007/978-981-15-6634-9_29
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