Abstract
Over the past 5 years due to the changes in the environmental and human lifestyle, several neurodevelopmental disorders are shooting their existence. Out of all, autism prevalence percentage is significant when compared to other neurodevelopmental disorders. To diagnose autism spectrum disorder medical experts, need big medical data and lot of time. Early detection of autism at an early age helps in curing the disorder through medication. Certain machine learning models are developed to predict autism in high-dimensional datasets. Dimensionality reduction is applied to identify predominant features that effect autism. Feature extraction is done by Chi-square test. Feature subsets are extracted using, mutual information and lightgbm. Later, the performances of the machine learning algorithms are analysed to find out suitable classifier that predicts the autism spectrum disorder. Using feature subsets extraction methodologies, the numbers of features are reduced from 802 to 254 features; hence, the performance of Naïve Bayesian and K-nearest neighbour has improved. Decision tree classifier has performed the best in predicting autism spectrum disorder with a testing accuracy of 97.47% and model training time of 23.508 s.
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References
Abdelhamid N, Padmavathy A, Peebles D, Thabtah F, Goulder-Horobin D (2020) Data imbalance in autism prediagnosis classification systems: an experimental study. J Inf Knowl Manag 19:2040014
Biswas M, et al. (2021) An XAI based autism detection: the context behind the detection. In: Brain informatics: 14th international conference, BI 2021, virtual event, Springer, Berlin, pp 448–459
Child and Adolescent Health Measurement Intiative (2013) 2020–2022 National survey of children’s health (2 years combined), [(SAS/SPSS/Stata)] indicator data set. In: Data resource center for child and adolescent health supported by cooperative agreement from the U.S; Department of Health and Human Services, Health Resources and services Administration (HRSA). Maternal and Child Health Bureau (MCHB), Washington
Deepa B, JeenMarseline KS (2019) Exploration of autism spectrum disorder using classification algorithms. Proc Comput Sci 165:143–150
Hassan MM, Mokhtar HMO (2019) Investigating autism etiology and heterogeneity by decision tree algorithm. Inf Med Unlocked 16:100215
He S, Liu R (2021) Developing a new autism diagnosis process based on a hybrid deep learning architecture through analysing home videos. arXiv preprint arXiv:2104.01137
Jaiswal S, et al. (2017) Automatic detection of ADHD and ASD from expressive behaviour in RGBD data. In: 2017 12th IEEE international conference on automatic face and gesture recognition (FG 2017), pp 762–769. https://doi.org/10.1109/FG.2017.95
Lawi A, Aziz F (2018) Comparison of classification algorithms of the autism Spectrum disorder diagnosis. In: Proceedings - 2nd East Indonesia conference on computer and information technology: internet of things for industry, EIConCIT 2018, pp 218–222
Liao M, Duan H, Wang G (2022) Application of machine learning techniques to detect the children with autism spectrum disorder. J Healthc Eng 25(2022):9340027
Misman MF, et al. (2019) Classification of adults with autism spectrum disorder using deep neural network. In: Proceedings - 2019 1st international conference on artificial intelligence and data sciencespp 29–34
Morris-Rosendahl DJ, Crocq MA (2020) Neurodevelopmental disorders-the history and future of a diagnostic concept. Dialogues Clin Neurosci 22(1):65–72. https://doi.org/10.31887/DCNS.2020.22.1/macrocq
Peral J, Gil D, Rotbei S, Amador S, Guerrero M, Moradi H (2020) A machine learning and integration based architecture for cognitive disorder detection used for early autism screening. Electronics 9:3
Raina SK, Chander V, Bhardwaj AK, Kumar D, Sharma S, Kashyap V, Singh M, Bhardwaj A (2017) Prevalence of autism spectrum disorder among rural, urban, and tribal children (1–10 years of age). J Neurosci Rural Pract 8(3):368–374. https://doi.org/10.4103/jnrp.jnrp_329_16
Raj S, Masood S (2020) Analysis and detection of autism spectrum disorder using machine learning techniques. Proc Comput Sci 167:994–1004
Stevens E, Dixon DR, Novack MN, Granpeesheh D, Smith T, Linstead E (2019) Identification and analysis of behavioural phenotypes in autism spectrum disorder via unsupervised machine learning. Int J Med Inf 129:29
Ugoni A, Walker BF (1995) The Chi square test: an introduction. COMSIG Rev 4(3):61–64
Wang H, Li L, Chi L, Zhao Z (2019) Autism screening using deep embedding representation. Lect Notes Comput Sci 11537:160–173
Zheng Y, Deng T, Wang Y (2021) Autism classification based on logistic regression model. In: 2021 IEEE 2nd international conference big data, artificial intelligence internet things engineering, ICBAIE 2021, pp 579–582
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Archana, P., Sirisha, G.N.V.G. & Chaitanya, R.K. Prediction of autism spectrum disorder from high-dimensional data using machine learning techniques. Soft Comput 27, 11869–11875 (2023). https://doi.org/10.1007/s00500-023-08657-0
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DOI: https://doi.org/10.1007/s00500-023-08657-0