Effective Autism Spectrum Disorder Prediction to Improve the Clinical Traits using Machine Learning Techniques

Effective Autism Spectrum Disorder Prediction to Improve the Clinical Traits using Machine Learning Techniques

  IJETT-book-cover           
  
© 2022 by IJETT Journal
Volume-70 Issue-4
Year of Publication : 2022
Authors : Dr. R. Surendiran, Dr. M. Thangamani, C. Narmatha, M. Iswarya
DOI :  10.14445/22315381/IJETT-V70I4P230

How to Cite?

Dr. R. Surendiran, Dr. M. Thangamani, C. Narmatha, M. Iswarya, "Effective Autism Spectrum Disorder Prediction to Improve the Clinical Traits using Machine Learning Techniques," International Journal of Engineering Trends and Technology, vol. 70, no. 4, pp. 343-359, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I4P230

Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental complaint that influences an individual’s communication, announcement, and knowledge talents. Analysis of Autism can be completed at any age-group level. Autism patients look at diverse kinds of disputes learning disabilities, and complexity with meditation. Mental health problems, motor difficulties, and sensory problems are some of the problems faced by Autism patients. Earlier diagnosis and proper medication at the early stage are essential to control ASD. The ASD prediction framework is built to support a behavioral aspect-based analysis model without any device in this research. The ASD prediction process is focused on the childhood and adolescent analysis model utilized in the system. The behavioral parameters are collected with the support of the Autism Query collections. The decision tree (DT) and Support Vector Machine (SVM) techniques, K-Nearest Neighbors (KNN), and Artificial Neural Network (ANN) are applied for the ASD prediction process. The Correlated Feature selection based Random Forest (CFS-RT) algorithm is applied for the ASD prediction process, giving an accuracy of 93.03%, and ANN produces 97.68% and outperformance other methods.

Keywords
Autism Spectrum Disorder, Decision Tree, Machine Learning, Data Mining, Support Vector Machine.

Reference
[1] M. Thangamani, R. Vijayalakshmi, M. Ganthimathi, Rajita, P. Malarkodi, S. Nallusamy, Efficient classification of heart disease using K-Means clustering Algorithm, International Journal of Engineering Trends and Technology, 68(12) (2020) 48-53.
[2] N. Suresh Kumar, M. Thangamani, V. Sasikumar S. Nallusamy, An Improved Machine Learning Approach for Predicting Ischemic Stroke, An Improved Machine Learning Approach for Predicting Ischemic Stroke, 68(1)(2021) 111-115.
[3] Thangamani M., Ganthimathi M., Sridhar S.R., Akila M., and Keerthana R. Engineering, Detecting Coronavirus Contact using Internet of things., International Journal of Pervasive Computing and Communications, 16(5) (2020) 447-456. https://doi.org/10.1108/IJPCC07-2020-0074.
[4] Dilantha Haputhanthri, Gunavaran Brihadiswaran, Sahan Gunathilaka, Dulani Meedeniya, and Sampath Jayarathna, An EEG based Channel Optimized Classification Approach for Autism Spectrum Disorder, Moratuwa Engineering Research Conference (Mercon), (2019).
[5] Surendiran, R., and Alagarsamy,K., 2010. Skin Detection Based Cryptography in Steganography (SDBCS) .International Journal of Computer Science and Information Technologies (IJCSIT), 1(4) (2010) 221-225.
[6] Supriya H S, Sumanth Alva R*, Suprith K P, Vikas Kumar L, and P Kyshan Neheeth, applying supervised learning technique to diagnose autism spectrum disorder (asd), International Journal of Recent Scientific Research , 11(5b) (2020) 38439-38441. DOI: http://dx.doi.org/10.24327/ijrsr.2020.1105.5312.
[7] Inon Wiratsin, L. Narupiyakul, Feature Selection Technique for Autism Spectrum Disorder, Proceedings of the 5th International Conference on Control Engineering and Artificial Intelligence, DOI, 10.1145/3448218.3448241, (2021).
[8] Surendiran, R., 2017. Development of Multi-Criteria Recommender System, IJETT International Journal of Economics and Management Studies (IJEMS) ISSN: 2393 - 9125, 4(1) (2017) 28-33.
[9] Daniel Bone, Somer Bishop, Matthew P. Black, and Shrikanth S. Narayanan, Use of machine learning to improve autism screening and diagnostic instruments: effectiveness, efficiency, and multi-instrument fusion, Journal of Child Psychology and Psychiatry, (2016).
[10] Lucia Billeci, Antonio Narzisi, Alessandro Tonacci, Beatrice Sbriscia-Fioretti, Luca Serasini, Francesca Fulceri, Fabio Apicella, Federico Sicca, Sara Calderoni & Filippo Muratori An integrated EEG and eye-tracking approach for the study of responding and initiating joint attention in Autism Spectrum Disorders, Scientific Reports, 7 (2017) 1-13.
[11] Filippo Muratori, Lucia Billeci, Sara Calderoni, Maria Boncoddo, Caterina Lattarulo, Valeria Costanzo, Marco Turi, Costanza Colombi and Antonio Narzisi, How Attention to Faces and Objects Changes Over Time in Toddlers with Autism Spectrum Disorders: Preliminary Evidence from An Eye-Tracking Study, Brain science, MDPI, 9(344) (2019) 1-11.doi:10.3390/brainsci9120344.
[12] S. Jayarathna, Y. Jayawardana, M. Jaime, and S. Thapaliya, Electroencephalogram (EEG) for Delineating Objective Measure of Autism Spectrum Disorder, in Computational Models for Biomedical Reasoning and Problem Solving, IGI Global, (2019) 34-65.
[13] S. Thapaliya, S. Jayarathna, and M. Jaime, Evaluating the EEG and Eye Movements for Autism Spectrum Disorder, in IEEE International Conference on Big Data (Big Data), (2018) 2328-2336.
[14] Surendiran, R., Similarity Matrix Approach in Web Clustering .Journal of Applied Science and Computations (JASC),5(1) (2018) 267272.
[15] Narzisi, A. Posada, M. Barbieri, F. Chericoni, N. Ciuffolini, D. Pinzino, M. Romano, R. Scattoni, M.L.Tancredi, R. Calderoni, S., Prevalence of autism spectrum disorder in a large Italian catchment area: A school-based population study within the ASDEU project. Epidemiol. Psychiatr. Sci. (2018).
[16] E. Grossi, C. Olivieri, and M. Buscema, Diagnosis of autism through EEG processed by advanced computational algorithms: A pilot study, Comput. Methods Programs Biomed., 142 (2017) 73-79.
[17] W. J. Bosl, H. Tager-Flusberg, and C. A. Nelson, EEG Analytics for Early Detection of Autism Spectrum Disorder: A data-driven approach, Scientific Reports, 8(1) (2018) 1-20.
[18] Zhong Zhao, Xiaobin Zhang, Wenzhou Li, Xinyao Hu, Xingda Qu, Xiaolan Cao, Yanru Liu, and Jianping Lu, Applying Machine Learning to Identify Autism With Restricted Kinematic Features, IEEE Access, (2019).
[19] J. Baio et al., Prevalence of Autism Spectrum Disorder Among Children Aged 8 Years - Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2014, MMWR. Surveillance Summaries, 67(6) (2018) 1-23.
[20] Surendiran, R., Rajan, K.P. and Sathish Kumar, M., Study on the Customer targeting using Association Rule Mining. International Journal on Computer Science and Engineering, 2(7) (2010) 2483-2484.
[21] Charlotte Küpper, Sanna Stroth, Nicole Wolff, Florian Hauck, Tanja Schad-Hansjosten, Luise poustka, Veit Roessner, Katharina Schultebraucks & Stefan Roepke, identifying predictive features of autism spectrum disorders in a clinical sample of adolescents and Adults using machine learning, Scientific report, nature research, 10(4805) (2020). doi.org/10.1038/s41598-020-61607-w.
[22] NaziaJassim, Simon Baron-Cohen, and John Suckling, Meta-analytic evidence of differential prefrontal and early sensory cortex activity during non-social sensory perception in Autism, Neuroscience and Biobehavioural Reviews, Elsevier, 127 (2021) 146-157.
[23] Suman Raj and Sarfaraz Masood, Analysis and Detection of Autism Spectrum Disorder Using Machine Learning Techniques, Procedia Computer Science, Elsevier, 167 (2020) 994-1004.
[24] Judith Charpentier, Marianne Latinus, Frederic Andersson, Agathe Saby, Jean-Philippe Cottier, Frederique Bonnet-Brilhault, EmmanuelleHouy-Durand, and Marie Gomot, Brain Correlates of Emotional Prosodic Change Detection in Autism Spectrum Disorder, Neuro Image: Clinical, Elsevier, 28 (2020) 102512.
[25] Surendiran, R. and Alagarsamy, K., PCA-based geometric modeling for automatic face detection. Int. J. Comput. Sci. Inform. Technol, 1(4) (2010) 221-225.
[26] Jennie Hayes, Tamsin Ford, Hateem Rafeeque, Ginny Russell, Clinical practice guidelines for the Diagnosis of Autism Spectrum Disorder in Adults and Children in The UK: a narrative review, BMC Psychiatry, 13(18) (2018) doi: 10.1186/s12888-018-1800-1.
[27] Mengyi Liao, Hengyao Duan, and Guangshuai Wang, Application of Machine Learning Techniques to Detect the Children with Autism Spectrum Disorder, Hindawi Journal of Healthcare Engineering, 2022 (2022) 1-10, Article ID 9340027.https://doi.org/10.1155/2022/9340027.
[28] Chelsea M. Parlett-Pelleriti, Elizabeth Stevens, Dennis Dixon, Erik J. Linstead, Applications of Unsupervised Machine Learning in Autism Spectrum Disorder Research: a Review, Journal of Autism and Developmental Disorders, (2022) 1-17. https://doi.org/10.1007/s40489-021-00299.
[29] Cooper J. Mellema 1,2, Kevin P. Nguyen 1,2, Alex Treacher 1 & Albert Montillo, Reproducible neuroimaging features for diagnosis of Autism Spectrum Disorder with Machine Learning, Scientific Reports, 0123456789 (2022) 1-13.https://doi.org/10.1038/s41598-02206459-2.
[30] Yinghan Zhu, Hironori Nakatani, Walid Yassin, Norihide Maikusa, Naohiro Okada, Akira Kunimatsu, Osamu Abe, Hitoshi Kuwabara, Hidenori Yamasue, Kiyoto Kasai, Kazuo Okanoya, and Shinsuke Koike, Application of a Machine Learning Algorithm for Structural Brain Images in Chronic Schizophrenia to Earlier Clinical Stages of Psychosis and Autism Spectrum Disorder: A Multiprotocol Imaging Dataset Study, Schizophrenia Bulletin, 10 (2022) 1-12.
[31] N. Sciaraffa, G. Borghini, P. Aricò, Joint Analysis of Eye Blinks and Brain Activity to Investigate Attentional Demand during a Visual Search Task, Brain Science, MDPI, 11(562) (20210) 1-20. DOI:10.3390/brainsci11050562.
[32] Bram van den Bekerom, Using Machine Learning for Detection of Autism Spectrum Disorder, IEEE, (2017).
[33] Fadi Thabtah, Autism Spectrum Disorder Screening: Machine Learning Adaptation and DSM-5 Fulfillment, ACM, (2017).
[34] Rasool Azeem Musa, Mehdi Ebadi Manaa2, and Ghassan Abdul-Majeed Predicting Autism Spectrum Disorder (ASD) for Toddlers and Children Using Data Mining Techniques, Journal of Physics: Conference Series, IOP publishing, doi:10.1088/1742-6596/1804/1/012089, 1804 (2021).
[35] Antoine Frigaux ,Joëlle Lighezzolo-Alnot , Jean-Claude Maleval and Renaud Evrard, Differential diagnosis on the Autism Spectrum: Theorizing an Ordinary Autism, L’evolution ´ psychiatrique, Elsevier, 86(2021) e1–e24.