International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 14, No. 4, 8 Aug. 2022
Cover page and Table of Contents: PDF (size: 983KB)
Development of an Interactive Dashboard for Analyzing Autism Spectrum Disorder (ASD) Data using Machine Learning
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Author(s)
Avishek Saha
1,*
Dibakar Barua
1
Mahbub C. Mishu
1
Ziad Mohib
1
Sumaya Binte Zilani Choya
1
Index Terms
Autism, CNN, SVM, Machine Learning, Data Mining, Tableau, python
Abstract
Autism Spectrum Disorder (ASD) is a neuro developmental disorder that affects a person's ability to communicate and interact with others for rest of the life. It affects a person's comprehension and social interactions. Furthermore, people with ASD experience a wide range of symptoms, including difficulties while interacting with others, repeated behaviors, and an inability to function successfully in other areas of everyday life. Autism can be diagnosed at any age and is referred to as a "behavioral disorder" since symptoms usually appear in the life's first two years. The majority of individuals are unfamiliar with the illness and so don't know whether or not a person is disordered. Rather than aiding the sufferer, this typically leads to his or her isolation from society. The problem with ASD starts in childhood and extends into adolescence and adulthood. In this paper, we studied 25 research articles on autism spectrum disorder (ASD) prediction using machine learning techniques. The data and findings of those publications using various approaches and algorithms are analyzed. Techniques are primarily assessed using four publicly accessible non-clinically ASD datasets. We found that support vector machine (SVM) and Convolutional Neural Network (CNN) provides most accurate results compare to other techniques. Therefore, we developed an interactive dashboard using Tableau and Python to analyze Autism data.
Cite This Paper
Avishek Saha, Dibakar Barua, Mahbub C. Mishu, Ziad Mohib, Sumaya Binte Zilani Choya, "Development of an Interactive Dashboard for Analyzing Autism Spectrum Disorder (ASD) Data using Machine Learning", International Journal of Information Technology and Computer Science(IJITCS), Vol.14, No.4, pp.14-24, 2022. DOI:10.5815/ijitcs.2022.04.02
Reference
[1]D. H. Oh, I. B. Kim, S. H. Kim, and D. H. Ahn, "Predicting autism spectrum disorder using blood-based gene expression signatures and machine learning," Clin. Psychopharmacol. Neurosci., vol. 15, no. 1, pp. 47–52, 2017.
[2]S. H. Lee, M. J. Maenner, and C. M. Heilig, "A comparison of machine learning algorithms for the surveillance of autism spectrum disorder," PLoS One, vol. 14, no. 9, p. e0222907, 2019.
[3]D. M. Blei, A. Y. Ng, and M. I. Jordan, "Latent dirichlet allocation," the Journal of machine Learning research, vol. 3, pp. 993–1022, 2003.
[4]C. Lord, M. Elsabbagh, G. Baird, J. Veenstra-Vanderweele, "autism spectrum disorder". The lancet. 2018 Aug 11;392(10146):508-20.
[5]P. T. Shattuck, A. M. Roux, L. E. Hudson, J. L. Taylor, M. J. Maenner, J. F. Trani, "Services for adults with an autism spectrum disorder". The Canadian Journal of Psychiatry, 57(5), 284-291, 2012.
[6]M. R. Kundu and M. S. Das, “Predicting autism spectrum disorder in infants using machine learning," in Journal of Physics: Conference Series, vol. 1362, no. 1. IOP Publishing, 2019, p. 01, 2018.
[7]B. Tyagi, R. Mishra and N. Bajpai, "Machine Learning Techniques to Predict Autism Spectrum Disorder," IEEE Punecon, pp. 1-5, doi: 10.1109/PUNECON.2018.8745405, 2018.
[8]D. Eman and A. W. R. Emanuel, "Machine Learning Classifiers for Autism Spectrum Disorder: A Review," 4th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), pp. 255-260, 2019
[9]Kamel, M. I., Alhaddad, M. J., Malibary, H. M., Thabit, K., Dahlwi, F., Alsaggaf, E. A., & Hadi, A. A. EEG based autism diagnosis using regularized Fisher Linear Discriminant Analysis. International Journal of Image, Graphics and Signal Processing, 4(3), 35., 2012.
[10]U Frith, F. Happé, "Autism spectrum disorder". Current biology. Oct 11;15(19):R786-90, 2005.
[11]S. Guang, N. Pang, X. Deng, L. Yang, F. He, L. Wu, C. Chen, F. Yin, J. Peng, "Synaptopathology involved in autism spectrum disorder". Frontiers in cellular neuroscience. Dec 21;12:470, 2018.
[12]L.A. Vismara, S.J. Rogers, "Behavioral treatments in autism spectrum disorder: what do we know?". Annual review of clinical psychology. Apr 27;6(1):447-68, 2010.
[13]G. Powell, S. V. Wass, J. T. Erichsen, and S. R. Leekam, "First evidence of the feasibility of gaze-contingent attention training for school children with autism," Autism, vol. 20, no. 8, pp. 927–937, 2016.
[14]M. Mythili and A. Shanavas, "A study on autism spectrum disorders using classification techniques," International Journal of Soft Computing and Engineering, vol. 4, no. 5, pp. 88–91, 2014.
[15]S. Qiu, Y. Lu, Y. Li, J. Shi, H. Cui, Y. Gu, Y. Li, W. Zhong, X. Zhu, Y. Liu et al., "Prevalence of autism spectrum disorder in asia: A systematic review and meta-analysis," Psychiatry research, vol. 284, p. 112679, 2020.
[16]A. A. Abdullah, S. Rijal, and S. R. Dash, "Evaluation on machine learning algorithms for classification of autism spectrum disorder (asd)," in Journal of Physics: Conference Series, vol. 1372, no. 1. IOP Publishing, 2019, p. 012052.
[17]K. S. Omar, P. Mondal, N. S. Khan, M. R. K. Rizvi, and M. N. Islam, "A machine learning approach to predict autism spectrum disorder," in 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE). IEEE, pp. 1–6, 2019.
[18]M. Duda, R. Ma, N. Haber, and D. Wall, "Use of machine learning for behavioral distinction of autism and adhd," Translational psychiatry, vol. 6, no. 2, pp. e732–e732, 2016.
[19]S. Raj and S. Masood, "Analysis and detection of autism spectrum dis-order using machine learning techniques," Procedia Computer Science, vol. 167, pp. 994–1004, 2020.
[20]S. B. Shuvo, J. Ghosh, and A. S. Oyshi, "A data mining based approach to predict autism spectrum disorder considering behavioral attributes," in 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT). IEEE, pp. 1–5, 2019.
[21]G. Devika Varshini and R. Chinnaiyan, “Optimized machine learning classification approaches for prediction of autism spectrum disorder," Ann Autism Dev Disord. 2020; 1 (1), vol. 1001, 2020.
[22]K. S. Omar, P. Mondal, N. S. Khan, M. R. K. Rizvi, and M. N. Islam, "A machine learning approach to predict autism spectrum disorder," in 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), 2019.
[23]B. Deepa and K. S. Jeen Marseline, "Exploration of autism spectrum disorder using classification algorithms," Procedia Comput. Sci., vol. 165, pp. 143–150, 2019.
[24]R. A. Musa, M. E. Manaa, and G. Abdul-Majeed, "Predicting autism spectrum disorder (ASD) for toddlers and children using data mining techniques," J. Phys. Conf. Ser., vol. 1804, no. 1, p. 012089, 2021.
[25]T. Akter et al., "Machine learning-based models for early stage detection of autism spectrum disorders," IEEE Access, vol. 7, pp. 166509–166527, 2019.
[26]Alwidian, Jaber & Elhassan, Ammar & Rawan, Ghnemat. "Predicting Autism Spectrum Disorder using Machine Learning Technique". 2277-3878. 10.35940/ijrte.E6016.018520, 2020.
[27]S. B. Imandoust And M. Bolandraftar. "Application of K-Nearest Neighbour (KNN)Approach for predicting Economic Events: Theoretical Background". Int. Journal of Engineering Research and Application, vol. 3, Issue 5, pp. 605-610,2013.
[28]"Autism screening data for toddlers," https://www.kaggle.com/fabdelja/autism-screening-for-toddlers.