Predicting Autism Spectrum Disorder Using Machine Learning Classifiers

TL;DR

This paper evaluates multiple machine learning classifiers for early ASD detection, finding SVM with Gaussian Radial Kernel achieves 95% accuracy on a standard dataset, aiding early diagnosis.

Contribution

It compares various classifiers for ASD prediction and identifies SVM with Gaussian Radial Kernel as the most effective method.

Findings

SVM with Gaussian Radial Kernel achieves 95% accuracy.

Support Vector Machine outperforms other classifiers.

The study uses a publicly available ASD dataset.

Abstract

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.