# Efficient Machine Learning Models for Early Stage Detection of Autism Spectrum Disorder
## 1 Introduction  
Autism spectrum disorder (ASD) is a neuro-developmental disorder where it appears in human beings during the first three years [1]. It is basically characterized by several symptoms such as impairments in social interaction, communication, restricted interests, and repetitive behavior [2]. Individuals with ASD face difficulty understanding other's feelings and thinking. They experience many problems communicating with others. As reported by the World Health Organization (WHO), throughout the world, around 1 in 270 individuals has ASD [3]. Each individual with ASD has unique characteristics, and some have exceptional abilities in visual, academic, and music skills. In this case, the most important steps are required to detect ASD and to ensure proper treatment as early as possible. These steps are helpful to decrease the effects of this disorder and to improvetheir condition. The symptoms of ASD are identified by different types of observations. However, a significant amount of time and effort are needed where early detection is useful for providing better treatment for ASD patients. Recently, machine learning methods are widely used for analyzing the symptoms of various severe diseases like heart disease, diabetes, and cancer tissues, etc. Therefore, many researchers explored numerous methods [4; 5] that are helpful to identify ASD patients and decrease the affects of ASD more precisely.  
The objective of this work was to propose a machine learning model that explores ASD of toddlers, children, adolescents, and adults at an early stage as well as to investigate the individual characteristics of them more efficiently. In this model, we generated several feature subsets of toddlers, children, adolescents, and adults using various feature selection techniques. Different classification methods were applied into primary and its feature subsets. Then, their results were compared, and we determined the best classifier for each age group and the feature subset for which the highest results were obtained. Additionally, the performance of these classifiers were investigated using a non-parametric statistical significant test. Then, we interpreted the results of the best feature subsets and selected significant features of ASD using an explainable AI method. The following concise summary of the contribution is given as follows:  
* We proposed an efficient machine learning method that has the potential to identify ASD with high accuracy at an early stage.
* We concentrated on identifying important feature subsets and explained different features to know how individual features are responsible to generate the best result to diagnose ASD or not.
* To justify the performance of the classifier, we used a non-parametric statistical method and checked the classifier's pairwise significance.
* This method is helpful to identify ASD in a simple and flexible way.  
This article is structured as follows: Section 2 contains a literature review on ASD screening approaches. Section 3 represents the working steps of detecting ASD and its characteristics. Section 4 describes the experimental results and an interpretation of these results. Section 5 describes the discussion and conclusion.