Machine learning algorithm for improving performance on 3 AQ-screening classification
Article Sidebar
Main Article Content
Abstract
Autism Spectrum Disorder (ASD) classification using machine learning can help parents, caregivers, psychiatrists, and patients to obtain the results of early detection of ASD. In this study, the dataset used is the autism-spectrum quotient for child, adolescent and adult, namely AQ-child, AQ-adolescent, AQ-adult. This study aims to improve the sensitivity and specificity of previous studies so that the classification results of ASD are better characterized by the reduced misclassification. The algorithm applied in this study: support vector machine (SVM), random forest (RF), artificial neural network (ANN). The evaluation results using 10-fold cross validation showed that RF succeeded in producing higher adult AQ sensitivity, which was 87.89%. The increase in the specificity level of AQ-Adolescents is better produced using an SVM of 86.33%.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright
Open Access authors retain the copyrights of their papers, and all open access articles are distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided that the original work is properly cited.
The use of general descriptive names, trade names, trademarks, and so forth in this publication, even if not specifically identified, does not imply that these names are not protected by the relevant laws and regulations.
While the advice and information in this journal are believed to be true and accurate on the date of its going to press, neither the authors, the editors, nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein.
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
2. C. Plauche and P. Care, Identification and evaluation of children with autism spectrum, Am. Acad. Pediat. 120 (2007) 1183-1215.
3. C. Lord, S. Risi, P. S. DiLavore, C. Shulman, A. Thurm, and A. Pickles, Autism from 2 to 9 years of age, Arch. Gen. Psychiatry. 63 (2006) 694–701.
4. CDC, Screening and Diagnosis of Autism Spectrum Disorder. [Online]. Available: https://www.cdc.gov/ncbddd/autism/screening.html. [Accessed: 10-Apr-2019].
5. C. W. Rishel, C. Greeno, S. C. Marcus, M. K. Shear, and C. Anderson, Use of the child behavior checklist as a diagnostic screening tool in community mental health, Res. Soc. Work Pract. 15 (2005) 195–203.
6. P. A. DA Krug, JR Arick, Autism screening instrument for educational planning-ASIEP 2, J. Psychoeduc. Assess. 16 (1998) 280-285.
7. C. Allison, B. Auyeung, and S. Baron-cohen, Toward brief ‘ red flags’ for autism screening?: the short autism spectrum quotient and the short quantitative, J. Am. Acad. Child. Adolesc. Psychiatry. 51 (2012) 202–212.e7.
8. National Institute for Health and Care Excellence, Autism spectrum disorder in adults: diagnosis and management, 2012. [Online]. Available: https://www.nice.org.uk/guidance/cg142.
9. Autism Research Centre, Downloadable Tests, 2012. [Online]. Available: https://www.autismresearchcentre.com/arc_tests.
10. F. Thabtah, F. Kamalov, and K. Rajab, A new computational intelligence approach to detect autistic features for autism screening, International J. Med. Inf. 117 (2018) 112–124.
11. J. A. Kosmicki, V. Sochat, M. Duda, and D. P. Wall, Searching for a minimal set of behaviors for autism detection through feature selection-based machine learning, Trans. Psychiat. 5 (2015) e514.
12. M. J. Maenner, M. Yeargin-Allsopp, K. N. Van Braun, D. L. Christensen, and L. A. Schieve, Development of a machine learning algorithm for the surveillance of autism spectrum disorder, PLoS One. 11 (2016) 1–11.
13. F. Zhang, P. Savadjiev, W. Cai, Y. Song, Y. Rathi, B. Tunç, D. Parker, T. Kapur, R. T. Schultz, N. Makris, R. Verma, and L. J. O’Donnell, Whole brain white matter connectivity analysis using machine learning: An application to autism, Neurolmage, 172 (2017) 826–837.
14. N. A. Almansour, H. F. Syed, N. R. Khayat, R. K. Altheeb, R. E. Juri, J. Alhiyafi, S. Alrashed, and S. O. Olatunji, Neural network and support vector machine for the prediction of chronic kidney disease: A comparative study, Comp. Biol. Med. 109 (2019) 101-111.
15. C. C. Wu, W. C. Yeh, W. D. Hsu, M. M. Islam, P. A. (Alex) Nguyen, T. N. Poly, Y. C. Wang, H. C. Yang, and Y. C. (Jack) Li, Prediction of fatty liver disease using machine learning algorithms, Comp. Biol. Med. 170 (2019) 23–29.
16. F. Thabtah, Autistic Spectrum Disorder Screening Data for Children Data Set, 2017 .[Online] .Available: http://archive.ics.uci.edu/ml/datasets/Autistic+Spectrum+Disorder+Screening+Data+for+Children++. [Accessed: 10-Apr-2019].
17. F. Thabtah, Autistic Spectrum Disorder Screening Data for Adolescent Data Set, 2017. [Online]. Available: https://archive.ics.uci.edu/ml/datasets/Autistic+Spectrum+Disorder+Screening+Data+for+Adolescent+++. [Accessed: 10-Apr-2019].
18. F. Thabtah, Autistic Spectrum Disorder Screening Data for Adult Data Set, 2017. [Online]. Available: https://archive.ics.uci.edu/ml/datasets/Autism+Screening+Adult. [Accessed: 10-Apr-2019].
19. D. McSherry. Strategic induction of decision trees bt - research and development in expert systems xv. Res. Dev. Expert Syst. XV, 1999.
20. Huan Liu and R. Setiono, Chi2: feature selection and discretization of numeric attributes, IEEE 7th Int. Conferene on Tools with Artificial Intelligence, 1995, pp. 388–391.
21. Y. Tang and J. Zhou, The performance of PSO-SVM in inflation forecasting,12 th Int. Conf. Serv. Syst. Serv. Manag. ICSSSM, 2015, pp. 1–4.
22. L. Breiman, Random forests, J. Mach. Learn. 45 (2001) 5–32.
23. I. Witten and E. Frank. Data mining 2nd edition: Practical machine learning tools and techniques. US of America: Morgan Kaufmann. 2005.
24. Scikit-learn Machine Learning in Python. [Online]. Available: https://scikit-learn.org/stable/. [Accessed: 10-Apr-2019].