Predicting Autism Spectrum Disorder Using Machine Learning Classifiers
This work addresses the need for earlier ASD identification to improve health outcomes, but it appears incremental as it applies existing methods to a known dataset.
This paper tackled the problem of predicting Autism Spectrum Disorder (ASD) by implementing several machine learning classifiers, with Support Vector Machine (SVM) using a Gaussian Radial Kernel achieving the best result of 95% accuracy on a standard dataset.
Autism Spectrum Disorder (ASD) is on the rise and constantly growing. Earlier identify of ASD with the best outcome will allow someone to be safe and healthy by proper nursing. Humans can hardly estimate the present condition and stage of ASD by measuring primary symptoms. Therefore, it is being necessary to develop a method that will provide the best outcome and measurement of ASD. This paper aims to show several measurements that implemented in several classifiers. Among them, Support Vector Machine (SVM) provides the best result and under SVM, there are also some kernels to perform. Among them, the Gaussian Radial Kernel gives the best result. The proposed classifier achieves 95% accuracy using the publicly available standard ASD dataset.