Abstract:

In recent years, autism spectrum disorder has gradually become a major global public health problem due to complex etiology and large group, and its prevalence rate has been rising all over the world. A series of explorations and discussions, which focused on effective intervention, have been conducted in this field. Early detection, early diagnosis and early intervention have been determined as the consensus of autistic children education and rehabilitation. However, both the limitations of traditional identification and diagnosis methods and the lack of professionals mostly caused the miss of the well-timed intervention in autistic children. However, traditional identification and diagnosis methods often have some limitations (e.g., strong subjectivity, time-consuming) and face the situation that lack of professionals and higher requirements for professional clinical ability, which seriously affect the early intervention of autistic children and even make them miss the best opportunity of education and rehabilitation; Furthermore, which brings huge psychological and economic burden to autistic children and their families. In order to improve this situation, machine learning has made great achievement in recent years. With its advantages of objectivity, accuracy, simplicity and flexibility, machine learning has been gradually applied in the early prediction, screening, diagnosis and evaluation of autism. Previous studies were mostly conducted on the basis of traditional machine learning algorithms (e.g., support vector machines, naive bayes and random forest) or deep learning algorithms (e.g.,, convolutional neural network, artificial neural network and multilayer perceptions), and the analysis of EEG signals, behavioral data and genetic data could support for establishing predictive or classification models which contribute to predict, identify and diagnose autistic children. Compared with the traditional classification, this method could not only improve the efficiency and reduce the pressure of professionals, but also automatically extract and sort out subtle and potentially critical information from a large amount of data. Moreover, it could gain insight into the inherent laws of complex problems, build a multimodal classification model, complement each other's advantages, and improve the classification accuracy. However, machine learning has limitations in the selection of research objects, the extraction and collection of classified data, and the application of theoretical models. To solve the above problems, first of all, future research should clarify the pathology, etiology and course factors of autism in the early stage of development, and promote the construction of a tracking database of pathophysiological information of pregnant women and newborn. The data collection of the health information of pregnant women and their family could know about related genetic and environmental teratogenic factors and try to transfer "passive waiting" to "active defense" and avoid obstacles as soon as possible. Secondly, it is very important to sort out the criteria and basis for selecting classification indicators and improve the fineness of indicators. For example, the appropriate classification indicators, which adapt to autistic children come from different age groups and different disability degree groups, should be determined. Following studies should gradually detectestable, widely adaptable and higher accurate indicators. The build of a more scientific and stable standardized classification index system could promote the internal order of classification indexes. Finally, the actual needs and worries of different audiences should be carefully investigated. The problems such as sample representativeness, model applicability, algorithm accuracy and stability should be solved as well. The extension of propaganda about theoretical research achievements is needed; Meanwhile, more efforts should be taken to accelerate the transformation from theoretical achievements of intelligent autism identification and diagnosis to practice.

Key words: machine learning, autism, early identification and diagnosis, systematic review