A Dissertation on Detection of Autism Spectrum Disorder by Machine Learning Algorithm

Bristy, Afsana Hossain; Hasan, Tasnimul; Shawon, Md Minhajul Islam

IUT Repository Home
→
Electrical and Electronics Engineering (EEE)
→
Thesis
→
Undergraduate
→
2022
→
View Item

dc.contributor.author	Bristy, Afsana Hossain
dc.contributor.author	Hasan, Tasnimul
dc.contributor.author	Shawon, Md Minhajul Islam
dc.date.accessioned	2023-01-05T09:25:05Z
dc.date.available	2023-01-05T09:25:05Z
dc.date.issued	2022-05-30
dc.identifier.citation	[1] T. Akter et al., “Machine learning-based models for early stage detection of autism spectrum disorders,” IEEE Access, vol. 7, pp. 166509–166527, 2019. [2] F. Thabtah, “Machine learning in autistic spectrum disorder behavioral research: A review and ways forward,” Informatics for Health and Social Care, vol. 44, no. 3. Taylor and Francis Ltd, pp. 278–297, Jul. 03, 2019, doi: 10.1080/17538157.2017.1399132. [3] K. Akyol, “Assessing the importance of autistic attributes for autism screening,” Expert Syst., vol. 37, no. 5, pp. 1–10, 2020, doi: 10.1111/exsy.12562. [4] S. Raj and S. Masood, “Analysis and Detection of Autism Spectrum Disorder Using Machine Learning Techniques,” Procedia Comput. Sci., vol. 167, no. 2019, pp. 994– 1004, 2020, doi: 10.1016/j.procs.2020.03.399. [5] S. Akhter, A. H. M. E. Hussain, J. Shefa, G. K. Kundu, F. Rahman, and A. Biswas, “Prevalence of Autism Spectrum Disorder (ASD) among the children aged 18-36 months in a rural community of Bangladesh: A cross sectional study [version 1; referees: 1 approved, 2 approved with reservations],” F1000Research, vol. 7, no. May, pp. 1–15, 2018, doi: 10.12688/f1000research.13563.1. [6] K. D. Rajab, A. Padmavathy, and F. Thabtah, “Machine Learning Application for Predicting Autistic Traits in Toddlers,” Arab. J. Sci. Eng., vol. 46, no. 4, pp. 3793– 3805, 2021, doi: 10.1007/s13369-020-05165-3. [7] F. Thabtah, “Autism spectrum disorder screening: Machine learning adaptation and DSM-5 fulfillment,” in ACM International Conference Proceeding Series, May 2017, vol. Part F1293, pp. 1–6, doi: 10.1145/3107514.3107515. [8] M. S. Mythili and A. R. M. Shanavas, “A Study on Autism Spectrum Disorders using Classification Techniques,” Int. J. Soft Comput. Eng., no. 5, pp. 2231–2307, 2014, [Online]. Available: http://www.ijsce.org/wpcontent/uploads/papers/v4i5/E2433114514.pdf. [9] C. Allison, B. Auyeung, and S. Baron-Cohen, “Toward brief ‘red flags’ for autism screening: The short Autism Spectrum Quotient and the short Quantitative Checklist in 1,000 cases and 3,000 controls,” J. Am. Acad. Child Adolesc. Psychiatry, vol. 51, no. 2, pp. 202-212.e7, 2012, doi: 10.1016/j.jaac.2011.11.003. [10] F. Thabtah, “An accessible and efficient autism screening method for behavioural data and predictive analyses,” Health Informatics J., vol. 25, no. 4, pp. 1739–1755, 2019, 59 doi: 10.1177/1460458218796636. [11] C. Küpper et al., “Identifying predictive features of autism spectrum disorders in a clinical sample of adolescents and adults using machine learning,” Sci. Rep., vol. 10, no. 1, pp. 1–11, 2020, doi: 10.1038/s41598-020-61607-w. [12] K. S. Oma, P. Mondal, N. S. Khan, M. R. K. Rizvi, and M. N. Islam, “A Machine Learning Approach to Predict Autism Spectrum Disorder,” 2nd Int. Conf. Electr. Comput. Commun. Eng. ECCE 2019, no. April, 2019, doi: 10.1109/ECACE.2019.8679454. [13] R. Vaishali and R. Sasikala, “A machine learning based approach to classify Autism with optimum behaviour sets,” Int. J. Eng. Technol., vol. 5, no. x, pp. 1–6, 2017, doi: 10.14419/ijet.v7i3.18.14907Published. [14] E. T. Prud’hommeaux, B. Roark, L. M. Black, and J. van Santen, “Classification of atypical language in autism,” Acl Hlt 2011, no. June, p. 88, 2011. [15] K. T. Quach, “Application of Artificial Neural Networks in Classification of Autism Diagnosis Based on Gene Expression Signatures,” 2012. [16] A. Genkin, D. D. Lewis, and D. Madigan, “Large-scale bayesian logistic regression for text categorization,” Technometrics, vol. 49, no. 3, pp. 291–304, 2007, doi: 10.1198/004017007000000245. [17] P. Mohan and I. Paramasivam, “Feature reduction using SVM-RFE technique to detect autism spectrum disorder,” Evol. Intell., no. 0123456789, 2020, doi: 10.1007/s12065- 020-00498-2. [18] H. Wang, L. Li, L. Chi, and Z. Zhao, “Autism Screening Using Deep Embedding Representation,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 11537 LNCS, pp. 160–173, 2019, doi: 10.1007/978- 3-030-22741-8_12. [19] K. Akyol, Y. Gultepe, and A. Karaci, “A Study on Autistic Spectrum Disorder for Children Based on Feature Selection and Fuzzy,” Int. Congr. Engieneering Life Sci., no. October, pp. 804–807, 2018. [20] M. Thomas and A. Chandran, “Artificial Neural Network for Diagnosing Autism Spectrum Disorder,” Proc. 2nd Int. Conf. Trends Electron. Informatics, ICOEI 2018, vol. 3, no. 2, pp. 930–933, 2018, doi: 10.1109/ICOEI.2018.8553781. [21] O. Altay and M. Ulas, “Prediction of the autism spectrum disorder diagnosis with linear discriminant analysis classifier and K-nearest neighbor in children,” 6th Int. Symp. Digit. Forensic Secur. ISDFS 2018 - Proceeding, vol. 2018-Janua, pp. 1–4, 60 2018, doi: 10.1109/ISDFS.2018.8355354. [22] G. Lucarelli and M. Borrotti, Artificial Intelligence Applications and Innovations, vol. 559, no. 691154. Cham: Springer International Publishing, 2019. [23] N. Goel, B. Grover, Anuj, D. Gupta, A. Khanna, and M. Sharma, “Modified Grasshopper Optimization Algorithm for detection of Autism Spectrum Disorder,” Phys. Commun., vol. 41, p. 101115, 2020, doi: 10.1016/j.phycom.2020.101115. [24] S. B. Shuvo, J. Ghosh, and A. S. Oyshi, “A Data Mining Based Approach to Predict Autism Spectrum Disorder Considering Behavioral Attributes,” 2019 10th Int. Conf. Comput. Commun. Netw. Technol. ICCCNT 2019, pp. 1–5, 2019, doi: 10.1109/ICCCNT45670.2019.8944905. [25] A. Choudhury and C. M. Greene, “Prognosticating Autism Spectrum Disorder Using Artificial Neural Network: Levenberg-Marquardt Algorithm,” arXiv, vol. 2, no. 6, 2018, doi: 10.26502/acbr.50170058. [26] N. Chakrabarty and S. Biswas, “Navo Minority Over-sampling Technique (NMOTe): A Consistent Performance Booster on Imbalanced Datasets,” J. Electron. Informatics, vol. 2, no. 2, pp. 96–136, 2020, doi: 10.36548/jei.2020.2.004. [27] M. M. Nishat et al., “Detection of Autism Spectrum Disorder by Discriminant Analysis Algorithm,” Lect. Notes Data Eng. Commun. Technol., vol. 95, pp. 473–482, 2022, doi: 10.1007/978-981-16-6636-0_36. [28] M. J. Zaki and W. Meira, Jr, “Linear Discriminant Analysis,” Data Min. Mach. Learn., pp. 501–516, 2020, doi: 10.1017/9781108564175.025. [29] A. Singh, B. S. Prakash, and K. Chandrasekaran, “A comparison of linear discriminant analysis and ridge classifier on Twitter data,” Proceeding - IEEE Int. Conf. Comput. Commun. Autom. ICCCA 2016, pp. 133–138, 2017, doi: 10.1109/CCAA.2016.7813704. [30] https://www.geeksforgeeks.org/ml-extra-tree-classifier-for-feature-selection/ [31] https://www.javatpoint.com/data-preprocessing-machine-learning [32] https://medium.com/analytics-vidhya/why-hyper-parameter-tuning-is-important-foryour-model-1ff4c8f145d3 [33] https://www.javatpoint.com/k-nearest-neighbor-algorithm-for-machine-learning [34] https://www.upgrad.com/blog/gaussiannaivebayes/#:~:text=What%20is%20Gaussian%20Na%C3%AFve%20Bayes,theorem %20with%20strong%20independence%20assumptions. [35] https://iq.opengenus.org/bernoulli-naive-bayes/ 61 [36] https://www.upgrad.com/blog/multinomial-naive-bayesexplained/#:~:text=The%20Multinomial%20Naive%20Bayes%20algorithm%20is%20 a%20Bayesian%20learning%20approach,tag%20with%20the%20greatest%20chance. [37] https://www.geeksforgeeks.org/ml-linear-discriminantanalysis/#:~:text=Linear%20discriminant%20analysis%20(LDA)%20is,values%2C%2 0which%20form%20a%20template. [38] https://medium.com/machine-learning-researcher/dimensionality-reduction-pca-andlda-6be91734f567 [39] https://www.geeksforgeeks.org/quadratic-discriminant-analysis/ [40] https://www.datascienceblog.net/post/machine-learning/linear-discriminant-analysis/ [41] https://datacadamia.com/data_mining/discriminant_analysis_quadratic [42] https://towardsdatascience.com/going-beyond-the-simpleimputer-for-missing-dataimputation-dd8ba168d505 [43] https://towardsdev.com/how-to-identify-missingness-types-with-missingno61cfe0449ad9 [44] https://www.analyticsvidhya.com/blog/2020/07/knnimputer-a-robust-way-toimpute-missing-values-using-scikit-learn/ [45] https://www.jair.org/index.php/jair/article/view/10302 [46] https://arxiv.org/abs/1106.1813 [47] https://machinelearningmastery.com/smote-oversampling-for-imbalancedclassification/	en_US
dc.identifier.uri	http://hdl.handle.net/123456789/1631
dc.description	Supervised by Dr. Md. Ashraful Hoque Professor, Dean Faculty of Engineering, Department of Electrical and Electronic Engineering, Islamic University of Technology, Co-Supervisor: Mirza Muntasir Nishat Assistant Professor and Fahim Faisal Assistant Professor Department of Electrical and Electronic Engineering Islamic University of Technology(IUT), This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2022.	en_US
dc.description.abstract	This work proposes an investigative strategy to examining the efficacy of alternative boosting algorithms in terms of improving the accuracy of diagnosing Autism Spectrum Disorder (ASD). When it comes to autism spectrum disorder (ASD), the average number of cases per 10,000 people has increased from 1.9 in 1980 to 14.8 in 2010, according to data from Asia. Early detection and identification are crucial for improved treatment outcomes in ASD. Different boosting machine learning algorithms have been shown to be an effective technique for detecting autism spectrum disorder (ASD) when it is still in its early stages. This research utilized a dataset containing a total of 1100 instances that was amalgamated from three datasets, with 104 instances being teenagers and 704 instances being adults, with the remainder of the instances being kid instances, from the University of California Irvine (UCI) repository. This dataset was used to train and test the model classification classifier. To begin the functioning of the classifiers, several methodologies were used to build distinct data frames and correlation heatmaps, which were then compared. Eight machine learning methods were investigated, and their performance parameters such as the confusion matrix and accuracy were measured and compared to one another. Furthermore, a comprehensive comparative research was carried out by simulating the precision, sensitivity, F1 score, and ROC-AUC of each algorithm and comparing the results.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Electrical and Electronic Engineering(EEE), Islamic University of Technology(IUT),	en_US
dc.subject	ASD,UCI,KNN,GNB,MNB,BNB,LDA,QDA,ML	en_US
dc.title	A Dissertation on Detection of Autism Spectrum Disorder by Machine Learning Algorithm	en_US
dc.type	Thesis	en_US