ARTIFICIAL NEURAL NETWORKS BASED-PREDICTION OF AUTISM SPECTRUM DISORDER

Year 2020, Volume: 5 Issue: 2, 78 - 82, 31.12.2020

İlknur Ucuz , Ayla Uzun Cicek

Abstract

Aim: Autism Spectrum Disorders (ASD) is one of the important neurodevelopmental disorders. This study aimed to perform artificial-intelligence-based modeling based on the prenatal-perinatal factors, family history, and developmental characteristics, which are emphasized as risk factors for ASD in the literature. Materials and Methods: The study was designed with a retrospective management and data from 136 children with ASD and 143 healthy children were included. Results: According to the findings of the MLP model, the five most important factors were the mean age of first words (months), the mean age of head control (months), the mean age of sitting without support (months), history of autism in the family, and the mean paternal age at pregnancy (years), respectively. Overall percentages of the training and testing samples were 91.4% and 88.0%. AUC for the model was 0.922 for the separation of the autism and control groups. Conclusion:The proposed model is able to successfully differentiate patients with autism spectrum disorders from healthy individuals and identify factors associated with the disease.

Keywords

artificial neural networks, autism, prenatal risk factors, perinatal risk factors

Supporting Institution

Destekleyen kurum bulunmamaktadır

Thanks

Due to the support he provided during modeling, thank you very much to Prof. Dr. Cemil Çolak

References

• [1] C. Lord, M. Elsabbagh, G. Baird, and J. Veenstra-Vanderweele, "Autism spectrum disorder," The Lancet, vol. 392, pp. 508-520, 2018.
• [2] K. Sanchack and C. A. Thomas, "Autism spectrum disorder: Primary care principles," American family physician, vol. 94, pp. 972-979, 2016.
• [3] E. Fombonne, "The rising prevalence of autism," Journal of Child Psychology and Psychiatry, vol. 59, pp. 717-720, 2018.
• [4] K. Lyall, L. Croen, J. Daniels, M. D. Fallin, C. Ladd-Acosta, B. K. Lee, et al., "The changing epidemiology of autism spectrum disorders," Annual review of public health, vol. 38, pp. 81-102, 2017.
• [5] V. Courchesne, A.-A. S. Meilleur, M.-P. Poulin-Lord, M. Dawson, and I. Soulières, "Autistic children at risk of being underestimated: school-based pilot study of a strength-informed assessment," Molecular Autism, vol. 6, p. 12, 2015.
• [6] C. A. Labarrere, J. Woods, J. Hardin, G. Campana, M. Ortiz, B. Jaeger, et al., "Early prediction of cardiac allograft vasculopathy and heart transplant failure," American Journal of Transplantation, vol. 11, pp. 528-535, 2011.
• [7] H. Gardener, D. Spiegelman, and S. L. Buka, "Prenatal risk factors for autism: comprehensive meta-analysis," The British journal of psychiatry, vol. 195, pp. 7-14, 2009.
• [8] D. Rahul, "Machine learning in medicine," Circulation, vol. 132, pp. 1920-1930, 2015.
• [9] A. Ari and D. Hanbay, "Deep learning based brain tumor classification and detection system," Turkish Journal of Electrical Engineering & Computer Sciences, vol. 26, pp. 2275-2286, 2018.
• [10] M. C. Çolak, C. Çolak, H. Kocatürk, S. Sagiroglu, and İ. Barutçu, "Predicting coronary artery disease using different artificial neural network models/Koroner arter hastaliginin degisik yapay sinir agi modelleri ile tahmini," Anadulu Kardiyoloji Dergisi: AKD, vol. 8, p. 249, 2008.
• [11] E. Güldoğan, T. Zeynep, and C. Çolak, "Classification of Breast Cancer and Determination of Related Factors with Deep Learning Approach," The Journal of Cognitive Systems, vol. 5, pp. 10-14.
• [12] E. Güldoğan, T. Zeynep, A. Ayça, and C. Çolak, "Performance Evaluation of Different Artificial Neural Network Models in the Classification of Type 2 Diabetes Mellitus," The Journal of Cognitive Systems, vol. 5, pp. 23-32.
• [13] J. Stewart, P. Sprivulis, and G. Dwivedi, "Artificial intelligence and machine learning in emergency medicine," Emergency Medicine Australasia, vol. 30, pp. 870-874, 2018.
• [14] A. Association, "American Psychiatric Association’s Diagnostic and statistical manual of mental disorders (DSM-V)," 2013.
• [15] F. Thabtah, "Machine learning in autistic spectrum disorder behavioral research: A review and ways forward," Informatics for Health and Social Care, vol. 44, pp. 278-297, 2019.
• [16] S. J. Moon, J. Hwang, R. Kana, J. Torous, and J. W. Kim, "Accuracy of Machine Learning Algorithms for the Diagnosis of Autism Spectrum Disorder: Systematic Review and Meta-Analysis of Brain Magnetic Resonance Imaging Studies," JMIR mental health, vol. 6, p. e14108, 2019.
• [17] Q. Tariq, J. Daniels, J. N. Schwartz, P. Washington, H. Kalantarian, and D. P. Wall, "Mobile detection of autism through machine learning on home video: A development and prospective validation study," PLoS medicine, vol. 15, p. e1002705, 2018.
• [18] A. Pratap and C. Kanimozhiselvi, "Predictive assessment of autism using unsupervised machine learning models," International Journal of Advanced Intelligence Paradigms, vol. 6, pp. 113-121, 2014.
• [19] M. Duda, R. Ma, N. Haber, and D. Wall, "Use of machine learning for behavioral distinction of autism and ADHD," Translational psychiatry, vol. 6, pp. e732-e732, 2016.
• [20] 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, p. e0222907, 2019.
• [21] D. P. Wall, J. Kosmicki, T. Deluca, E. Harstad, and V. A. Fusaro, "Use of machine learning to shorten observation-based screening and diagnosis of autism," Translational psychiatry, vol. 2, pp. e100-e100, 2012.
• [22] N. M. Rad, S. M. Kia, C. Zarbo, T. van Laarhoven, G. Jurman, P. Venuti, et al., "Deep learning for automatic stereotypical motor movement detection using wearable sensors in autism spectrum disorders," Signal Processing, vol. 144, pp. 180-191, 2018.
• [23] R. O. Bahado-Singh, S. Vishweswaraiah, B. Aydas, N. K. Mishra, A. Yilmaz, C. Guda, et al., "Artificial intelligence analysis of newborn leucocyte epigenomic markers for the prediction of autism," Brain research, vol. 1724, p. 146457, 2019.
• [24] M. I. U. Haque, "A Facial Expression Recognition Application Development Using Deep Convolutional Neural Network For Children With Autism Spectrum Disorder To Help Identify Human Emotions," 2019.
• [25] F. Thabtah, N. Abdelhamid, and D. Peebles, "A machine learning autism classification based on logistic regression analysis," Health information science and systems, vol. 7, p. 12, 2019.
• [26] C. Wang, H. Geng, W. Liu, and G. Zhang, "Prenatal, perinatal, and postnatal factors associated with autism: a meta-analysis," Medicine, vol. 96, 2017.
• [27] A. Modabbernia, E. Velthorst, and A. Reichenberg, "Environmental risk factors for autism: an evidence-based review of systematic reviews and meta-analyses," Molecular autism, vol. 8, p. 13, 2017.
• [28] P. Titelman, Differentiation of self: Bowen family systems theory perspectives: Routledge, 2014.
• [29] S. Mishra, D. Joshi, R. Ribeiro, and S. Anand, "Kinematics-coordinated walking pattern based on embedded controls," Journal of medical engineering & technology, vol. 34, pp. 329-334, 2010.