# Efficient Machine Learning Models for Early Stage Detection of Autism Spectrum Disorder
## 2 Literature Review  
Many state-of-art works were happened to investigate, classify and explore significant factors of ASD. [PERSON] et al. [6; 7; 8; 9] developed a mobile application named ASDTests for data collection related to ASD for toddlers, children, adolescents, and adults. This app was built based on Q-CHAT and AQ-10 tools to predict ASD or not. They collected ASD data using this app and uploaded them into the University of California-Irvine (UCI) Machine Learning (ML) repository. [PERSON] et al. [10] proposed an effective machine learning model where they analyzed AQ-10 and 250 real datasets with Random Forest (RF), Classification and Regression Trees (CART) and Random Forest-Iterative Dichotomiser 3 (ID3). [PERSON] et al. [11] investigated these datasets by employing CFS-greedy stepwise feature selector and further applied Naive Bayes (NB), Stochastic Gradient Descent (SGD), K-Nearest Neighbours (KNN), Random Tree (RT), and K-Star (KS) into these datasets. [PERSON] et al. [12] collected some samples of 16-30 years children where several tree based classifiers were used to investigate them and extracted several rules for normal and autism. [PERSON] et al. [13] analyzed similar datasets by implementing KNN, SVM, and RF where RF showed the best performance to identify ASD. Another study by [PERSON] et al. [14] generated several feature subsets of adults and adolescents using Information Gain (IG) and Chi-Squared (CHI) where Logistic Regression (LR) was used to identify ASD from them. [PERSON] et al. [15] gathered toddlers, children, adolescents, and adults datasets and generated some transformed sets. Then, different classifiers were used to analyze them where SVM showed the best performance for the toddler as well as Adaboost provided both children and adult. In addition, Glmboost showed the best outcomes the adolescent dataset. [PERSON] et al. [16] investigated similar types of datasets and generated subsets using CFS,CHI, IG, One-R and Relief-F methods. Further, they employed LR, Multilayer Perceptron (MLP), Sequential minimal optimization (SMO) into them. Then, SMO showed the best accuracy 91% for child, 99.9% for adolescent, 97.58% for adult datasets. [PERSON] et al. [17] scrutinized these datasets (i.e., excluding toddler) using SVM, LR, NB, and Convolutional Neural Network (CNN) where CNN showed the highest accuracy 98.30% for child, 96.88% for adolescent, and 99.53%, for adult, respectively. Again, [PERSON] et al. [18] developed a Rules-based Machine Learning (RML) method for extracting ASD traits where RML gave higher predictive accuracy from other machine learning approaches. [PERSON] et al. [19] provided an association classification technique with seven algorithms where this method showed 97% accuracy to detect ASD. [PERSON] et al. [20] gathered ASD dataset of different age levels and generated some transformed subsets of it. They analyzed them with several classifiers where LR outperformed other classifiers and extracted significant traits. [PERSON] et al. [21] also extracted several autism subtypes using k-means algorithms. Then, they identified different discriminatory factors among them.