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Paper ID:IJSREM64255

Early Detection of Autism Spectrum Disorder

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM64255

Volume:

10 |

5 |

May 2026

Pages:

1-8

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM64255

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

Abhinandan Hambirrao Suryawanshi

Abhinandan Hambirrao Suryawanshi

Department of Computer Science And Engineering

ASHOKRAO MANE GROUP OF INSTITUTION

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Prashant Pandurang Vadd

Prashant Pandurang Vadd

Department of Computer Science And Engineering

ASHOKRAO MANE GROUP OF INSTITUTION

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Shivtej Suhas Patil

Shivtej Suhas Patil

Department of Computer Science And Engineering

ASHOKRAO MANE GROUP OF INSTITUTION

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Sushil Pravin Godbole

Sushil Pravin Godbole

Department of Computer Science And Engineering

ASHOKRAO MANE GROUP OF INSTITUTION

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Jufen Farooq Torgal

Jufen Farooq Torgal

Department of Computer Science And Engineering

ASHOKRAO MANE GROUP OF INSTITUTION

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Mentors

PAP

Pooja Amrish Patil

Department of Computer Science And Engineering

ASHOKRAO MANE GROUP OF INSTITUTION

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Abstract

In present day autism spectrum disorder (ASD) is gaining its momentum faster than ever. Applying Image processing technique Detecting autism traits through screening tests is very expensive and time consuming. With the advancement of artificial intelligence and machine learning (ML), autism can be predicted at quite an early stage. Though a number of studies have been carried out using different techniques, these studies didn’t provide any definitive conclusion about predicting autism traits in terms of different age groups. Therefore, this paper aims to propose an effective prediction model based on ML technique and to develop a user interface forpredicting ASD for people of any age. As outcomes of this research, an autism prediction model was developed by merging Random Forest-CART (Classification and Regression Trees) and Random Forest-ID3 (Iterative Dichotomiser 3) and cnn algorithm also a user interface was developed based on the proposed prediction model. The proposed model was evaluated with AQ10 dataset and 250 real datasets collected from people with and without autistic traits.city, sensitivity, precision and false positive rate (FPR) for thThe evaluation results showed that the proposed prediction model provides better results in terms of accuracy, specifie data.

KEYWORDS

Image Processing Technique Random Forest CART Random Forest- ID3 AQ10 dataset False Positive Rate.

Cite This Article

Abhinandan Hambirrao Suryawanshi, Prashant Pandurang Vadd, Shivtej Suhas Patil, Sushil Pravin Godbole, Jufen Farooq Torgal, Pooja Amrish Patil, "Early Detection of Autism Spectrum Disorder," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-8, May 2026. https://doi.org/10.55041/IJSREM64255

Full Article/Conclusion

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that affects social interaction, communication, and behavior. It is a spectrum disorder, which means that the severity of symptoms and challenges can vary greatly among individuals with ASD.Research has shown that there is no single cause of ASD, but rather a combination of genetic and environmental factors. While there is no known cure for ASD, early intervention and treatment can help individuals with ASD lead fulfilling and productive lives.Individuals with ASD may face challenges in social interaction, communication, and behavior, but they also have unique strengths and abilities. It is important to recognize and appreciate these strengths, and to work to create a more inclusive and supportive society for individuals with ASD.Overall, ASD is a complex and varied disorder that requires individualized support and understanding. With the right resources and support, individuals with ASD can thrive and make valuable contributions to their communities. The integration of multimodal learning and decision fusion significantly improved overall system robustness and reduced false positive rates. The proposed framework demonstrates its effectiveness in detecting sophisticated multimedia forgeries and can be effectively applied in digital forensics, cybersecurity, social media verification, and online content authentication systems.

References

1. U. Frith and F. Happé, “Autism spectrum disorder,” Current biology, vol. 15, no. 19, pp. R786–R790, 2005. 1. WHO, Autism spectrum disorders, 2017 [Accessed August 22,       2018].    Available: http://www.who.int/news-room/fact-sheets/detail/ autism-spectrum-disorders. 2. J. A. Cruz and D. S. Wishart, “Applications of machine learning in cancer prediction and prognosis,” Cancer informatics, vol. 2, 2006. 3. N. S. Khan, M. H. Muaz, A. Kabir, and M. N. Islam, “Diabetes predicting mhealth application using machine learning,” in 2017 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE). IEEE, 2017, pp. 237–240. 4. D. P. Wall, R. Dally, R. Luyster, J.-Y. Jung, and T. F. DeLuca, “Use of artifificial intelligence to shorten the behavioral diagnosis of autism,” PloS one, vol. 7, no. 8, p. e43855, 2012. 5. D. Bone, S. L. Bishop, M. P. Black, M. S. Goodwin, C. Lord, and S. S. Narayanan, “Use of machine learning to improve autism screening and diagnostic instruments: effectiveness, effificiency, and multi-instrument fusion,” Journal of Child Psychology and Psychiatry, vol. 57, 2016. 6. 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," Journal of the American Academy of Child Adolescent Psychiatry, vol. 51, 2012. 7. F. Thabtah, “autism spectrum disorder screening: machine learning adaptation and dsm5 fulfillment,” in roceedings of the 1st International Conference on Medical and Health Informatics 2017. ACM, 2017. 8. F. Hauck and N. Kliewer, “Machine learning for autism diagnostics: Applying support vector classification.” 9.      B. van den Bekerom, “Using machine learning for detection of autism spectrum disorder,” 2017.

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Publication Date

5/29/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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