Using Machine Learning Techniques for Autism Spectrum Disorder Analysis and Detection in Children

Authors: Norah Alshahrani, Abdulaziz Almaleh

Abstract:

Autism Spectrum Disorder (ASD) is a condition related to issues with brain development that affects how a person recognises and communicates with others which results in difficulties with interaction and communication socially and it is constantly growing. Early recognition of ASD allows children to lead safe and healthy lives and helps doctors with accurate diagnoses and management of conditions. Therefore, it is crucial to develop a method that will achieve good results and with high accuracy for the measurement of ASD in children. In this paper, ASD datasets of toddlers and children have been analyzed. We employed the following machine learning techniques to attempt to explore ASD: Random Forest (RF), Decision Tree (DT), Na¨ıve Bayes (NB) and Support Vector Machine (SVM). Then feature selection was used to provide fewer attributes from ASD datasets while preserving model performance. As a result, we found that the best result has been provided by SVM, achieving 0.98% in the toddler dataset and 0.99% in the children dataset.

Keywords: Autism Spectrum Disorder, ASD, Machine Learning, ML, Feature Selection, Support Vector Machine, SVM.

References:

[1] Naila Z Khan, Lilia Albores Gallo, Aurora Arghir, Bogdan Budisteanu, Magdalena Budisteanu, Iuliana Dobrescu, Kirsty Donald, Samia El-Tabari, Michelle Hoogenhout, Fidelie Kalambayi, et al. Autism and the grand challenges in global mental health. 2012.
[2] Fabio Apicella, Valeria Costanzo, and Giulia Purpura. Are early visual behavior impairments involved in the onset of autism spectrum disorders? insights for early diagnosis and intervention. European Journal of Pediatrics, 179(2):225–234, 2020.
[3] Amanda J Baxter, TS Brugha, Holly E Erskine, Roman W Scheurer, Theo Vos, and James G Scott. The epidemiology and global burden of autism spectrum disorders. Psychological medicine, 45(3):601–613, 2015.
[4] L Mercer, S Creighton, JJA Holden, and MES Lewis. Parental perspectives on the causes of an autism spectrum disorder in their children. Journal of Genetic Counseling, 15(1):41–50, 2006.
[5] Michael D Kogan, Bonnie B Strickland, Stephen J Blumberg, Gopal K Singh, James M Perrin, and Peter C van Dyck. A national profile of the health care experiences and family impact of autism spectrum disorder among children in the united states, 2005–2006. Pediatrics, 122(6):e1149–e1158, 2008.
[6] U˘gur Erkan and Dang NH Thanh. Autism spectrum disorder detection with machine learning methods. Current Psychiatry Research and Reviews Formerly: Current Psychiatry Reviews, 15(4):297–308, 2019.
[7] Tania Akter, Md Shahriare Satu, Md Imran Khan, Mohammad Hanif Ali, Shahadat Uddin, Pietro Lio, Julian MW Quinn, and Mohammad Ali Moni. Machine learning-based models for early stage detection of autism spectrum disorders. IEEE Access, 7:166509–166527, 2019.
[8] Suman Raj and Sarfaraz Masood. Analysis and detection of autism spectrum disorder using machine learning techniques. Procedia Computer Science, 167:994–1004, 2020.
[9] Koushik Chowdhury and Mir Ahmad Iraj. Predicting autism spectrum disorder using machine learning classifiers. In 2020 international conference on recent trends on electronics, information, communication & technology (RTEICT), pages 324–327. IEEE, 2020.
[10] National Institue of Health Research. Autism research centre. http://docs.autismresearchcentre.com/tests/AQ10.pdf, Website.
[11] Md Delowar Hossain, Muhammad Ashad Kabir, Adnan Anwar, and Md Zahidul Islam. Detecting autism spectrum disorder using machine learning techniques. Health Information Science and Systems, 9(1):1–13, 2021.
[12] kaggle. Datasets. https://www.kaggle.com/datasets, Website.
[13] kaggle. behavior analysis of autism. https://www.kaggle.com/iashiqul/behavior-analysis-of-autism, Website.
[14] Kaggle. autism screening child. https://www.kaggle.com/datasets/basmarg/autism-screening-child-twoversion? select=Child-Data2017.csv.
[15] Nasiba Mahdi Abdulkareem, Adnan Mohsin Abdulazeez, et al. Machine learning classification based on radom forest algorithm: A review. International Journal of Science and Business, 5(2):128–142, 2021.
[16] Sayali D Jadhav and HP Channe. Comparative study of k-nn, naive bayes and decision tree classification techniques. International Journal of Science and Research (IJSR), 5(1):1842–1845, 2016.
[17] Batta Mahesh. Machine learning algorithms-a review. International Journal of Science and Research (IJSR).
[Internet], 9:381–386, 2020.