AIRCC's International Journal of Computer Science and Information Technology

Open Access Open Access  Restricted Access Subscription Access

Newly Proposed Technique for Autism Spectrum Disorder Based Machine Learning


Affiliations
1 Associate Professor Department of Communication and Computer Engineering, October University for Modern Sciences and Arts, Giza, Egypt
2 Master of Computer Science, Arab East Colleges for Graduate Studies, Riyadh, Saudi Arabia
 

The rapid growth in the number of autism disorder among toddlers needs for the development of easily implemented and effective screening methods. In this current era, the causes of Autism Spectrum Disorder (ASD) do not know yet, however, the diagnosis and detection of ASD is based on behaviours and symptoms. This paper aims to improve ASD disease prediction accuracy among toddlers by using the Logistic Regression model of Machine Learning, through the collected health care dataset and by using an algorithm for rapid classification of the behaviours to check whether the children are having autism diseases or not according to information in the dataset. Therefore, Machine Learning decreasing the time needed to detect the disorder, then providing the necessary health services early for infected toddlers to enhance their lifestyle. In healthcare, most machine learning applications are in the research stage, and to take the advantage of emerging software tools that incorporate artificial intelligence, healthcare organizations first need to overcome a variety of challenges.

Keywords

Artificial Intelligence, Machine Learning, Autism Spectrum Disorder.
User
Notifications
Font Size

  • Vaishali, R., and R. Sasikala. "A Machine Learning Based Approach to classify Autism with Optimum Behaviour Sets", (2018) International Journal of Engineering & Technology 7(4): 18.

  • Thabtah Fadi, Firuz Kamalov, Khairan Rajab “A New Computational Intelligence Approach to Detect Autistic Features for Autism Screening”, International journal of medical informatics, 117 (2018), pp. 112-124.

  • Thabtah, Fadi. "Machine Learning in Autistic Spectrum Disorder Behavioural Research: A review and ways forward”, (2018), Informatics for Health and Social Care: 1-20.

  • M.S. Mythili, AR Mohamed Shanavas “A study on Autism Spectrum Disorders using Classification Techniques”, International Journal of Soft Computing and Engineering (IJSCE), 4 (2014), pp. 88-91.

  • Dennis P. Wall, Rebecca Dally, Rhiannon Luyster, Jae-Yoon Jung, Todd F. DeLuca” Use of Artificial Intelligence to Shorten the Behavioural Diagnosis of Autism. PloS one, 7 (8) (2012), p. e43855.

  • Dennis Paul Wall, J. Kosmicki, T.F. Deluca, E. Harstad, Vincent Alfred Fusaro “Use of Machine Learning to Shorten Observation-based Screening and Diagnosis of Autism”, Translational psychiatry, 2 (4) (2012), p. e100.

  • F. Thabtah, “Autism Spectrum Disorder Screening: Machine Learning Adaptation and DSM-5 Fulfillment“, Journal of ACM Digital Library, No. 6, May 2017.

  • Bekerom, “Using Machine Learning for Detection of Autism Spectrum Disorder“,26th Twente Student Conf. on IT Feb 3th, No. 7, February 2017.

  • H. Alarif and G. Young, “Using Multiple Machine Learning Algorithms to Predict Autism in Children”, proc. of International Conf. on Artificial Intelligence (ICAI'18), No. 4, 2018.

  • A. DEMİRHAN, “Performance of Machine Learning Methods in Determining the Autism Spectrum Disorder Cases”, Mugla Journal of Science and Technology, No. 6, June 2018.

  • A. Muller, S. Guido, Introduction to Machine Learning with Python, O’Reilly Media, Sebastopol, 2016.

  • David W. Hosmer jr, Stanley Lemeshow, Rodney X. Sturdivant , “Applied Logistic Regression”, 3rd Edition – 2013.


Abstract Views: 179

PDF Views: 84




  • Newly Proposed Technique for Autism Spectrum Disorder Based Machine Learning

Abstract Views: 179  |  PDF Views: 84

Authors

Sherif Kamel
Associate Professor Department of Communication and Computer Engineering, October University for Modern Sciences and Arts, Giza, Egypt
Rehab Al-Harbi
Master of Computer Science, Arab East Colleges for Graduate Studies, Riyadh, Saudi Arabia

Abstract


The rapid growth in the number of autism disorder among toddlers needs for the development of easily implemented and effective screening methods. In this current era, the causes of Autism Spectrum Disorder (ASD) do not know yet, however, the diagnosis and detection of ASD is based on behaviours and symptoms. This paper aims to improve ASD disease prediction accuracy among toddlers by using the Logistic Regression model of Machine Learning, through the collected health care dataset and by using an algorithm for rapid classification of the behaviours to check whether the children are having autism diseases or not according to information in the dataset. Therefore, Machine Learning decreasing the time needed to detect the disorder, then providing the necessary health services early for infected toddlers to enhance their lifestyle. In healthcare, most machine learning applications are in the research stage, and to take the advantage of emerging software tools that incorporate artificial intelligence, healthcare organizations first need to overcome a variety of challenges.

Keywords


Artificial Intelligence, Machine Learning, Autism Spectrum Disorder.

References