Next Article in Journal
Access Control Role Evolution Mechanism for Open Computing Environment
Previous Article in Journal
A Fully-Integrated Analog Machine Learning Classifier for Breast Cancer Classification
Previous Article in Special Issue
An Efficient and Unique TF/IDF Algorithmic Model-Based Data Analysis for Handling Applications with Big Data Streaming
Open AccessFeature PaperArticle

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

1
Lucentia Research Group, Department of Software and Computing Systems, University of Alicante, 03690 Alicante, Spain
2
Lucentia Research Group, Department of Computer Science Technology and Computation, University of Alicante, 03690 Alicante, Spain
3
Department of Information Technology, Mehr Alborz University, 1413913141 Tehran, Iran
4
U.I. for Computer Research; 03690 Alicante, Spain
5
School of ECE, University of Tehran, 14395-515 Tehran, Iran
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(3), 516; https://doi.org/10.3390/electronics9030516
Received: 31 January 2020 / Revised: 15 March 2020 / Accepted: 19 March 2020 / Published: 21 March 2020
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. View Full-Text
Keywords: data mining; machine learning; data integration; autism spectrum disorder data mining; machine learning; data integration; autism spectrum disorder
Show Figures

Figure 1

MDPI and ACS Style

Peral, J.; Gil, D.; Rotbei, S.; Amador, S.; Guerrero, M.; Moradi, H. A Machine Learning and Integration Based Architecture for Cognitive Disorder Detection Used for Early Autism Screening. Electronics 2020, 9, 516.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop