Abstract
Children with autism spectrum disorders (ASD) have some disorder activities. Usually, they cannot speak fluently. Instead, they use signs and pointing words to make a relationship. One of the most challenging tasks for caregivers understands their needs but the early diagnosis of the disease can make it much easier. In the beginning stage the main problem begins with the kids and it keeps going on till adult. Thestudyextents of medical diagnosis, in this article there is an effort to discover the opportunity to use Logistic Regression, SVM, Naïve Bayes and RF for forecasting and investigate of ASD problems in a child. The important features of this area are covered and represented with the literature-based classification of the research. The main features of fMRI and an overview of ML’s general classification pipeline are offered.To check the generalizability of the outcome the entire data set and severe methods are necessary. ML technologies are expected to advance significantly in the coming years, contributing to the diagnosis of ASD and helping clinicians rapidly.
Keywords : Autism spectrum disorder (ASD), biomarkers, functional magnetic resonance imaging (fMRI), Machine Learning, KNN, Logistic Regression, SVM.
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