@conference{Abdullah2019,
title = {Evaluation on Machine Learning Algorithms for Classification of Autism Spectrum Disorder (ASD)},
author = {A A Abdullah and S Rijal and S R Dash},
editor = {Rahim Mustafa Zaaba Norali Noor S B A N B S K A N B A B M Fook C.Y. Yazid H.B.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076493636&doi=10.1088%2f1742-6596%2f1372%2f1%2f012052&partnerID=40&md5=2ec1bd9f6cf1e3afe965cc9e3792f536},
doi = {10.1088/1742-6596/1372/1/012052},
issn = {17426588},
year = {2019},
date = {2019-01-01},
journal = {Journal of Physics: Conference Series},
volume = {1372},
number = {1},
publisher = {Institute of Physics Publishing},
abstract = {Autism Spectrum Disorder (ASD) was characterized by delay in social interactions development, repetitive behaviors and narrow interest, which usually diagnosed with standard diagnostic tools such as Autism Diagnostic Observation Schedule (ADOS) and Autism Diagnostic Interview-Revised (ADIR-R). Previous work has implemented machine-learning methods for the classification of ASD, however they used different types of dataset such as brain images for MRI and EEG, risk genes in genetic profiles and behavior evaluation based on ADOS and ADI-R. Here a trial on using Autism Spectrum Questions (AQ) to build models that have higher potential to classify ASD was developed. In this research, Chi-square and Least Absolute Shrinkage and Selection Operator (LASSO) have been selected as feature selection methods to select the most important features for 3 supervised machine learning algorithms, which are Random Forest, Logistic Regression and K-Nearest Neighbors with K-fold cross validation. The performance was evaluated in which results Logistic Regression scored the highest accuracy with 97.541% using model with 13 selected features based on Chi-square selection method. © 2019 IOP Publishing Ltd. All rights reserved.},
note = {cited By 0},
keywords = {Autism Spectrum Disorders, Behaviour Evaluations, Biomedical Engineering, Brain Mapping, Classification (of information), Decision Trees, Diseases, Feature Extraction, Feature Selection Methods, K Fold Cross Validations, Learning, Least Absolute Shrinkage and Selection Operators, Least Squares Approximations, Logistic Regressions, Machine Learning, Machine Learning Methods, Magnetic Resonance Imaging, Nearest Neighbor Search, Regression Analysis, Supervised Learning, Supervised Machine Learning},
pubstate = {published},
tppubtype = {conference}
}

@conference{Shamsuddin2014387,
title = {Response of children with autism to robotic intervention and association with IQ levels},
author = {S Shamsuddin and H Yussof and F A Hanapiah and S Mohamed},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84920873999&doi=10.1109%2fDEVLRN.2014.6983012&partnerID=40&md5=1ae5aa42a315453fa73d5b927c2ff026},
doi = {10.1109/DEVLRN.2014.6983012},
isbn = {9781479975402},
year = {2014},
date = {2014-01-01},
journal = {IEEE ICDL-EPIROB 2014 - 4th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics},
pages = {387-393},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {This paper presents a qualitative observation on the initial response of children with autism when exposed to a humanoid robot. To elicit response, the robot autonomously executed 5 segments of interaction designed according to the triad impairments of autism. The aim was to observe the children's autistic behavior with a robot compared to their natural characteristics as observed in classroom setting. We also seek the association between responses to the robot with the children's intelligence level. Results with 12 children were analyzed to acquire relationship between initial responses and the children's IQ scores. Analysis indicates that the presence of the robot had significantly reduced the scores of autistic traits in the subscale of stereotyped behavior and communication. Behavior evaluation shows that children with IQ scores ranging from 80 to 109 were more receptive to robot-based intervention. © 2014 IEEE.},
note = {cited By 1},
keywords = {Anthropomorphic Robots, Autism, Behaviour Evaluations, Children with Autism, Classroom Settings, Diseases, Human Robot Interaction, Humanoid Robot, Intelligent Robots, IQ Level, Qualitative Observations, Robotics, Robots},
pubstate = {published},
tppubtype = {conference}
}