A Machine Learning and Integration Based Architecture for Cognitive Disorder Detection Used for Early Autism Screening

Abstract

About 15% of the world’s population suffers from some form of disability. In developed countries, about 1.5% of children are diagnosed with autism. Autism is a developmental disorder distinguished mainly by impairments in social interaction and communication and by restricted and repetitive behavior. Since the cause of autism is still unknown, there have been many studies focused on screening for autism based on behavioral features. Thus, the main purpose of this paper is to present an architecture focused on data integration and analytics, allowing the distributed processing of input data. Furthermore, the proposed architecture allows the identification of relevant features as well as of hidden correlations among parameters. To this end, we propose a methodology able to integrate diverse data sources, even data that are collected separately. This methodology increases the data variety which can lead to the identification of more correlations between diverse parameters. We conclude the paper with a case study that used autism data in order to validate our proposed architecture, which showed very promising results.

Diagnosis-Based Hybridization of Multimedical Tests and Sociodemographic Characteristics of Autism Spectrum Disorder Using Artificial Intelligence and Machine Learning Techniques: A Systematic Review

“…If individual factors are identified and checked for their severity, it is possible to prevent autism across the globe. A diagnosis process has been achieved in the study of [5] by integrating data from many diverse sources, such as medical or intervention centers, hospitals, and academic centers. It can facilitate the early diagnosis of autism patients.…”

“…In these contexts, many known data mining algorithms have been used or developed to solve this issue [56], such as the Bayesian fuzzy neural network proposed that was used in the study [65]. In addition, the customization of the ML allowed the merging of data from many varied sources, such as medical or intervention centers, hospitals, and academic centers, with help/support [5]. The authors' primary motivation in [59] recommended autism ontology, which is the major urge for developing algorithms based on subfield and is mainly used for calculating precision, pace, and customizability.…”

“…Thus, making the diagnosis of ASD an arduous task [57]. The study of [5] focused on screening issues for autism based on behavioral features.…”

“…According to the World Health Organization (WHO), every year, out of 160 children worldwide, one child is diagnosed with autism [4]. In developed countries, about 1.5% of children were diagnosed with ASD in 2017 [5]. Recently, autism research has gained more attention, including the early diagnosis process and investigation of affected features of this disease.…”

The Contribution of Machine Learning and Eye-Tracking Technology in Autism Spectrum Disorder Research: A Systematic Review

“…Eye-tracking instruments are often combined with modern artificial intelligence techniques, such as machine learning, a data driven technique, based on advanced learning of mathematics, statistical estimation, and theories of information [20] in which the computer algorithm is trained in order to analyse a set of data observed and learns the latent patterns in a statistical approach [20,21]. Machine learning can contribute to autism research by providing a less biased and reproducible second opinion [22], i.e., early autism screening [23] and diagnosis enhancement [20], as well as different behaviours [24] and brain activity observation [25]. Moreover, machine learning can be a valid biomarker-based technique that can contribute to objective ASD diagnosis [26].…”