An Integrated System for Initial Prediction of Autism Spectrum Disorder

doi:10.23940/ijpe.21.06.p2.504510

Abstract

Abstract: Autism Spectrum Disorder (ASD) neuro-developmental disarray incorporates numerous conduct issues like socializing and lack of communication. The traits of autism tend to show during early childhood and regularly last all through an individual's life. The symptoms include avoiding eye contact, needing to be separated from everyone else, and experiencing difficulty identifying with others or not having an interest in others. It also includes conditions like recursive examples of conduct, territories of interest, or exercises. A framework to record, identify and label the different patterns of conduct of people with ASD has been created. In this study, various sorts of classification methods for ASD diagnosis were used on the dataset consisting of people from different age groups. The classification techniques that we used were LDA (Linear Discriminant Analysis), Logistic regression and KNN (KNearest Neighbor). Using these methods, the findings suggest that the model performed best using Logistic Regression to predict Autism Spectrum Disorder with a precision of 99% during the validation trial.

Key words: Autism disorder, ADI-R, KNN, logistic regression, LDA, classification, AQ-Chat

Azhagiri.M, Shubhanjay Mishra, Shubham Joshi, and Amritash Srivastava. An Integrated System for Initial Prediction of Autism Spectrum Disorder [J]. Int J Performability Eng, 2021, 17(6): 504-510.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

References

1. Zhao Y., Zhao P., Liang H. and Zhang X.Identifying Genes Associated With Autism Spectrum Disorders by Random Walk Method With Significance Tests.IEEE Access, 8, pp.156686-156694, 2020.
2. Thabtah F.Machine learning in autistic spectrum disorder behavioral research: A review and ways forward. Informatics for Health and Social Care,44(3), pp.278-297, 2019.
3. Katuwal G.J. Machine Learning Based Autism Detection Using Brain Imaging, 2017.
4. Bone D., Goodwin M.S., Black M.P., Lee C.C., Audhkhasi K. and Narayanan S.Applying machine learning to facilitate autism diagnostics: pitfalls and promises. Journal of autism and developmental disorders,45(5), pp.1121-1136, 2015.
5. Wall D.P., Kosmicki J., Deluca T.F., Harstad E. and Fusaro V.A.Use of machine learning to shorten observation-based screening and diagnosis of autism. Translational psychiatry,2(4), pp.e100-e100, 2012.
6. Hauck, F. and Kliewer, N.Machine Learning for Autism Diagnostics: Applying Support Vector Classification. InInt’l Conf. Health Informatics and Medical Systems, 2017.
7. Heinsfeld A.S., Franco A.R., Craddock R.C., Buchweitz A. and Meneguzzi F.Identification of autism spectrum disorder using deep learning and the ABIDE dataset.NeuroImage: Clinical, 17, pp.16-23, 2018.
8. van den Bekerom, B. February. Using machine learning for detection of autism spectrum disorder. InProc. 20th Student Conf. IT (pp. 1-7), 2017.
9. Wall D.P., Kosmicki J., Deluca T.F., Harstad E. and Fusaro V.A.Use of machine learning to shorten observation-based screening and diagnosis of autism. Translational psychiatry,2(4), pp.e100-e100, 2012.
10. Liu, W., Li, M. and Yi, L.Identifying children with autism spectrum disorder based on their face processing abnormality: A machine learning framework. Autism Research,9(8), pp.888-898, 2016.
11. Bone D., Goodwin M.S., Black M.P., Lee C.C., Audhkhasi K. and Narayanan S.Applying machine learning to facilitate autism diagnostics: pitfalls and promises. Journal of autism and developmental disorders,45(5), pp.1121-1136, 2015.
12. Kosmicki J.A., Sochat V., Duda M. and Wall D.P.Searching for a minimal set of behaviors for autism detection through feature selection-based machine learning. Translational psychiatry,5(2), pp.e514-e514, 2015.
13. Wolfers T., Buitelaar J.K., Beckmann C.F., Franke B. and Marquand A.F.From estimating activation locality to predicting disorder: a review of pattern recognition for neuroimaging-based psychiatric diagnostics.Neuroscience & Biobehavioral Reviews, 57, pp.328-349, 2015.
14. Duda M., Ma R., Haber N. and Wall D.P.Use of machine learning for behavioral distinction of autism and ADHD. Translational psychiatry,6(2), pp.e732-e732, 2016.
15. Chu, K.C., Huang, H.J. and Huang, Y.S.August. Machine learning approach for distinction of ADHD and OSA. In 2016 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM) (pp. 1044-1049). IEEE, 2016.

[1]	Janarthanan Sekar and Ganesh Kumar T. Hyperparameter Tuning in Deep Learning-Based Image Classification to Improve Accuracy using Adam Optimization [J]. Int J Performability Eng, 2023, 19(9): 579-586.
[2]	Aashita Rajput, Muskan Yadav, Sachin Yadav, Megha Chhabra, and Arun Prakash Agarwal. Patch-Based Breast Cancer Histopathological Image Classification using Deep Learning [J]. Int J Performability Eng, 2023, 19(9): 607-623.
[3]	Rakesh Kumar, Sunny Arora, Ashima Arya, Neha Kohli, Vaishali Arya, and Ekta Singh. Ensemble Learning for Appraising English Text Readability using Gompertz Function [J]. Int J Performability Eng, 2023, 19(6): 388-396.
[4]	Vaishali Arya and Tapas Kumar. Boosting X-Ray Scans Feature for Enriched Diagnosis of Pediatric Pneumonia using Deep Learning Models [J]. Int J Performability Eng, 2023, 19(3): 175-183.
[5]	Harshita Batra and Leema Nelson. DCADS: Data-Driven Computer Aided Diagnostic System using Machine Learning Techniques for Polycystic Ovary Syndrome [J]. Int J Performability Eng, 2023, 19(3): 193-202.
[6]	Shalaka Prasad Deore. SongRec: A Facial Expression Recognition System for Song Recommendation using CNN [J]. Int J Performability Eng, 2023, 19(2): 115-121.
[7]	Shikha Choudhary and Bhawna Saxena. Image-Based Crop Disease Detection using Machine Learning Approaches: A Survey [J]. Int J Performability Eng, 2023, 19(2): 122-132.
[8]	Kamireddy Vijay Chandra, Kala Praveen Bagadi, Kalapala Vidya Sagar, R. Manjula Sri, and K. Sudha Rani. Deep Learning-Powered Corneal Endothelium Image Segmentation with Attention U-Net [J]. Int J Performability Eng, 2023, 19(11): 736-743.
[9]	Jitender Tanwar, Sanjay Kumar Sharma, Mandeep Mittal, and Ashok Kumar Yadav. Classification of Web Services for Efficient Performability [J]. Int J Performability Eng, 2023, 19(10): 654-662.
[10]	Priyanshu Verma, Ishan Sharma, Sonia Deshmukh, and Rohit Vashisht. Customer Churn Analysis using Spark and Hadoop [J]. Int J Performability Eng, 2023, 19(10): 663-675.
[11]	Sachin Jain and Vishal Jain. Ensemble Techniques for Classification of Brain Tumor Images Based on Weighting Average of Various Deep Learning-Based Components Models [J]. Int J Performability Eng, 2023, 19(10): 676-686.
[12]	Priti Kumari and Parmeet Kaur. An Adaptable Approach to Fault Tolerance in Cloud Computing [J]. Int J Performability Eng, 2023, 19(1): 43-54.
[13]	Sachin Aggarwal and Smriti Sehgal. Text Independent Data-Level Fusion Network for Multimodal Sentiment Analysis [J]. Int J Performability Eng, 2022, 18(9): 605-612.
[14]	Sandhya Alagarsamy and Visumathi James. RNN LSTM-based Deep Hybrid Learning Model for Text Classification using Machine Learning Variant XGBoost [J]. Int J Performability Eng, 2022, 18(8): 545-551.
[15]	Shobhanam Krishna and Sumati Sidharth. HR Analytics: Employee Attrition Analysis using Random Forest [J]. Int J Performability Eng, 2022, 18(4): 275-281.