ARCHIVES
Research Article
Early-Stage Detection of Autism Spectrum Using Machine Learning
Sneha A P1
Prashanth H V2
Dr. M. B. Anandaraju3
Sneha K R4
Anusha M N5
Varshini D B6
1,2,4,6Dept. of ECE, BGS Institute of Technology,Adichunchanagiri University, B.G Nagara, Karnataka, India. 3Professor, Dept. of ECE, BGS Institute of Technology, Adichunchanagiri University, B.G Nagara, Karnataka, India. 5Assistant Professor, Dept. of ECE,BGS Institute of Technology, Adichunchanagiri University, B.G Nagara, Karnataka, India.
Published Online: May-June 2024
Pages: 190-194
Cite this article
↗ https://www.doi.org/10.59256/ijire.20240503023References
1. M. Bala, M. H. Ali, M. S. Satu, K. F. Hasan, and M. A. Moni, ‘‘Efficient machine learning models for early stage detectionof autism
spectrum disorder,’’ Algorithms, vol. 15, no. 5, p. 166, May 2022.
2. D. Pietrucci, A. Teofani, M. Milanesi, B. Fosso, L Putignani, F. Messina,G. Pesole, A. Desideri, and G. Chillemi, ‘‘Machinelearning
data analysis highlights the role of parasutterella and alloprevotella in autism spectrum.disorders,’’ Biomedicines,vol. 10, no. 8, p.
2028, Aug. 2022.
3. R. Sreedasyam, A. Rao, N. Sachidanandan, N. Sampath, and S. K. Vasudevan, ‘‘Aarya—A kinesthetic companion for children with
autism spectrum disorder,’’ J. Intell. Fuzzy Syst., vol. 32, no. 4, pp. 2971–2976, Mar. 2017.
4. J. Amudha and H. Nandakumar, ‘‘A fuzzy based eye gaze pointestimation approach to study the task behavior in autism spec- trum
disorder,’’ J. Intell. Fuzzy Syst., vol. 35, no. 2, pp. 1459–1469, Aug. 2018.
5. H. Chahkandi Nejad, O. Khayat, and J. Razjouyan, “Software developmentof an intelligent spirography test system for neuro-logical
disorder detection and quantification,’’ J. Intell. Fuzzy Syst., vol. 28, no. 5, pp. 2149–2157, Jun. 2015.
6. F. Z. Subah, K. Deb, P. K. Dhar, and T. Koshiba, ‘‘A deep learning approach to predict autism spectrum disorder using multisite
resting-state fMRI,’’ Appl. Sci., vol. 11, no. 8, p. 3636, Apr. 2021.
7. K.-F. Kollias, C. K. Syriopoulou-Delli, P. Sarigiannidis, and G.F. Fragulis, ‘‘The contribution of machine learning and eye- tracking
technology in autism spectrum disorder research: A systematic review,’’ Electronics, vol. 10, no. 23, p. 2982, Nov.2021.
8. I. A. Ahmed, E. M. Senan, T. H. Rassem, M. A. H. Ali, H. S. A. Shatnawi, S. M. Alwazer, and M. Alshahrani, ‘‘Eye tracking-based
diagnosis and early detection of autism spectrum disorderusing machine learning and deep learning techniques,’’ Elec- tronics, vol.
11, no. 4, p. 530, Feb. 2022.
9. P. Sukumaran and K. Govardhanan, ‘‘Towards voice based prediction and analysis of emotions in ASD children,’’ J. Intell. Fuzzy
Syst., vol. 41, no. 5, pp. 5317–5326, 2021
10. S. P. Abirami, G. Kousalya, and R. Karthick, ‘‘Identification and ex- ploration of facial expression in children with ASD in a contact
less environment,’’ J. Intell. Fuzzy Syst., vol. 36, no. 3, pp. 2033–2042, Mar. 2019.
11. M. D. Hossain, M. A. Kabir, A. Anwar, and M. Z. Islam, ‘‘Detecting autism spectrum disorder using machine learningtechniques,’’
Health Inf. Sci. Syst., vol. 9, no. 1, pp. 1–13, Dec. 2021.
12. [12] 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. Amer. Acad.Child Adolescent Psychi-atry, vol. 51, no. 2,
pp. 202–212, 2012.
13. F.Thabtah,F. Kamalov, and K. Rajab, ‘‘A new computationalintelligence approach to detect autistic features for autism screening,’’
Int. J. Med. Inform., vol. 117, pp. 112–124, Sep.2018.
14. M. M. Ali, B. K. Paul, K. Ahmed, F. M. Bui, J. M. W. Quinn,M. A. Moni, ‘‘Heart disease prediction using Supervised ma-chine learning
algorithms: Performance analysis and compar-ison,’’ Comput. Biol. Med., vol. 136, Sep. 2021, Art. no. 104672.
15. E. Dritsas and M. Trigka, ‘‘Stroke risk prediction witma- chine learning techniques,’’ Sensors, vol. 22, no. 13, p.4670, Jun. 2022.
16. V. Chang, J. Bailey, Q. A. Xu, and Z. Sun, ‘‘Pima Indians diabetes mellitus classification based on machine learning (ML)
algorithms,’’ Neural Compute. Appl., early access, pp. 1–17, Mar. 2022.
17. F. Thabtah, ‘‘Machine learning in autistic spectrum disorderbehavioral research: A review and ways forward,’’ Inform.Health
Social Care, vol. 44, no. 3, pp. 278–297, 2018.
18. K. S. Omar, P. Mondal, N. S. Khan, M. R. K. Rizvi, and M.N.Islam.‘‘A machine learning approach to predict autism spec-trum
disorder,’’ in Proc. Int. Conf. Electro., Computer Com-munication. Eng. (ECCE), Feb. 2019, pp. 1–6.
19. H. Abbas, F. Garberson, E. Glover, and D. P. Wall, ‘‘Ma- chine learning approach for early detection of autism bycom-bining
questionnaire and home video screening,’’ J.Amer. Med. Informat. Assoc., vol. 25, no. 8, pp. 1000–1007, 2018.
20. K. L. Goh, S. Morris, S. Rosalie, C. Foster, T. Falkmer, andT.Tan‘‘Typically developed adults and adults with autism spec-trum
disorder classification using centre of pressure measure-ments,’’ in Proc.IEEE Int. Conf. Acoust., Speech Signal Pro-cess. (ICASSP),
Mar. 2016, pp. 844–848.
21. A. Crippa, C. Salvatore, P. Perego, S. Forti, M. Nobile, M. Molteni, and I. Castiglioni, ‘‘Use of machine learning to identify children
with autism and their motor abnormalities,’’J. Autism Develop. Disorders, pp. 2146–2156, 2015
spectrum disorder,’’ Algorithms, vol. 15, no. 5, p. 166, May 2022.
2. D. Pietrucci, A. Teofani, M. Milanesi, B. Fosso, L Putignani, F. Messina,G. Pesole, A. Desideri, and G. Chillemi, ‘‘Machinelearning
data analysis highlights the role of parasutterella and alloprevotella in autism spectrum.disorders,’’ Biomedicines,vol. 10, no. 8, p.
2028, Aug. 2022.
3. R. Sreedasyam, A. Rao, N. Sachidanandan, N. Sampath, and S. K. Vasudevan, ‘‘Aarya—A kinesthetic companion for children with
autism spectrum disorder,’’ J. Intell. Fuzzy Syst., vol. 32, no. 4, pp. 2971–2976, Mar. 2017.
4. J. Amudha and H. Nandakumar, ‘‘A fuzzy based eye gaze pointestimation approach to study the task behavior in autism spec- trum
disorder,’’ J. Intell. Fuzzy Syst., vol. 35, no. 2, pp. 1459–1469, Aug. 2018.
5. H. Chahkandi Nejad, O. Khayat, and J. Razjouyan, “Software developmentof an intelligent spirography test system for neuro-logical
disorder detection and quantification,’’ J. Intell. Fuzzy Syst., vol. 28, no. 5, pp. 2149–2157, Jun. 2015.
6. F. Z. Subah, K. Deb, P. K. Dhar, and T. Koshiba, ‘‘A deep learning approach to predict autism spectrum disorder using multisite
resting-state fMRI,’’ Appl. Sci., vol. 11, no. 8, p. 3636, Apr. 2021.
7. K.-F. Kollias, C. K. Syriopoulou-Delli, P. Sarigiannidis, and G.F. Fragulis, ‘‘The contribution of machine learning and eye- tracking
technology in autism spectrum disorder research: A systematic review,’’ Electronics, vol. 10, no. 23, p. 2982, Nov.2021.
8. I. A. Ahmed, E. M. Senan, T. H. Rassem, M. A. H. Ali, H. S. A. Shatnawi, S. M. Alwazer, and M. Alshahrani, ‘‘Eye tracking-based
diagnosis and early detection of autism spectrum disorderusing machine learning and deep learning techniques,’’ Elec- tronics, vol.
11, no. 4, p. 530, Feb. 2022.
9. P. Sukumaran and K. Govardhanan, ‘‘Towards voice based prediction and analysis of emotions in ASD children,’’ J. Intell. Fuzzy
Syst., vol. 41, no. 5, pp. 5317–5326, 2021
10. S. P. Abirami, G. Kousalya, and R. Karthick, ‘‘Identification and ex- ploration of facial expression in children with ASD in a contact
less environment,’’ J. Intell. Fuzzy Syst., vol. 36, no. 3, pp. 2033–2042, Mar. 2019.
11. M. D. Hossain, M. A. Kabir, A. Anwar, and M. Z. Islam, ‘‘Detecting autism spectrum disorder using machine learningtechniques,’’
Health Inf. Sci. Syst., vol. 9, no. 1, pp. 1–13, Dec. 2021.
12. [12] 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. Amer. Acad.Child Adolescent Psychi-atry, vol. 51, no. 2,
pp. 202–212, 2012.
13. F.Thabtah,F. Kamalov, and K. Rajab, ‘‘A new computationalintelligence approach to detect autistic features for autism screening,’’
Int. J. Med. Inform., vol. 117, pp. 112–124, Sep.2018.
14. M. M. Ali, B. K. Paul, K. Ahmed, F. M. Bui, J. M. W. Quinn,M. A. Moni, ‘‘Heart disease prediction using Supervised ma-chine learning
algorithms: Performance analysis and compar-ison,’’ Comput. Biol. Med., vol. 136, Sep. 2021, Art. no. 104672.
15. E. Dritsas and M. Trigka, ‘‘Stroke risk prediction witma- chine learning techniques,’’ Sensors, vol. 22, no. 13, p.4670, Jun. 2022.
16. V. Chang, J. Bailey, Q. A. Xu, and Z. Sun, ‘‘Pima Indians diabetes mellitus classification based on machine learning (ML)
algorithms,’’ Neural Compute. Appl., early access, pp. 1–17, Mar. 2022.
17. F. Thabtah, ‘‘Machine learning in autistic spectrum disorderbehavioral research: A review and ways forward,’’ Inform.Health
Social Care, vol. 44, no. 3, pp. 278–297, 2018.
18. K. S. Omar, P. Mondal, N. S. Khan, M. R. K. Rizvi, and M.N.Islam.‘‘A machine learning approach to predict autism spec-trum
disorder,’’ in Proc. Int. Conf. Electro., Computer Com-munication. Eng. (ECCE), Feb. 2019, pp. 1–6.
19. H. Abbas, F. Garberson, E. Glover, and D. P. Wall, ‘‘Ma- chine learning approach for early detection of autism bycom-bining
questionnaire and home video screening,’’ J.Amer. Med. Informat. Assoc., vol. 25, no. 8, pp. 1000–1007, 2018.
20. K. L. Goh, S. Morris, S. Rosalie, C. Foster, T. Falkmer, andT.Tan‘‘Typically developed adults and adults with autism spec-trum
disorder classification using centre of pressure measure-ments,’’ in Proc.IEEE Int. Conf. Acoust., Speech Signal Pro-cess. (ICASSP),
Mar. 2016, pp. 844–848.
21. A. Crippa, C. Salvatore, P. Perego, S. Forti, M. Nobile, M. Molteni, and I. Castiglioni, ‘‘Use of machine learning to identify children
with autism and their motor abnormalities,’’J. Autism Develop. Disorders, pp. 2146–2156, 2015
Related Articles
2024
Embedding Artificial Intelligence for Personal Voice Assistant Using NLP
2024
Analysis of Pedestrian Steel Bridge subjected the Seismic Load and Wind Load using Damper at different Span
2024
Review Paper on Comparison of Asymmetric and Symmetric RCC Building with Soil Structure Interaction by Dynamic Loading
2024
BLYNK RFID and Retinal Lock Access System
2024
ML-Driven Facial Synthesis from Spoken Words Using Conditional GANs
2024