Early Autism Diagnosis: Machine Learning Models and Their Effectiveness

[{“box”:0,”content”:”[if 992 equals=”Open Access”]n

n

n

n

Open Access

nn

n

n[/if 992]n

n

Year : August 20, 2024 at 3:31 pm | [if 1553 equals=””] Volume : [else] Volume :[/if 1553] | [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] : | Page : –

n

n

n

n

n

n

By

n

[foreach 286]n

vector

n

n

Poonam Chaudhary, Anmol Bhatia, Vanshika Sangwan, Riya Saxena, Rahul, Sanyam Virmani,

n

    n t

  • n

n

n[/foreach]

n

n[if 2099 not_equal=”Yes”]n

    [foreach 286] [if 1175 not_equal=””]n t

  1. Student, Student, Student, Student, Student, Student Department of Computer Science and Engineering, The NorthCap University, Gurugram, Department of Computer Science and Engineering, The NorthCap University, Gurugram, Department of Computer Science and Engineering, The NorthCap University, Gurugram, Department of Computer Science and Engineering, The NorthCap University, Gurugram, Department of Computer Science and Engineering, The NorthCap University, Gurugram, Department of Computer Science and Engineering, The NorthCap University, Gurugram Haryana, Haryana, Haryana, Haryana, Haryana, Haryana India, India, India, India, India, India

    2. n[/if 1175][/foreach]

n[/if 2099][if 2099 equals=”Yes”][/if 2099]n

n

Abstract

nDiagnosis is of utmost importance for timely intervention and support. However, traditional diagnosis methods, which are based on subjective assessment, are delayed. This project explores the role that machine learning techniques might play in enhancing the accuracy and effectiveness of ASD detection. Several state-of-the-art classification algorithms were benchmarked using a dataset from Kaggle. Logistic Regression, XG Boost, Random Forest, Decision Tree, and Gradient Boosting were taken into consideration. Other performance measures, in terms of accuracy, F1-score, and precision, were considered. The results showed that XG Boost was the best model, because this one had the most precision and reliability of ASD prediction. The research signifies the potential of AI and ML technologies for the betterment of the diagnostic process and provides a robust and timely tool for early detection of ASD. Conclusions and recommendations of the study strongly emphasize the necessity of approaches that integrate multidisciplinary and ethical considerations for responsible translation into clinical practice.

n

n

n

Keywords: Autism spectrum disorder (ASD), Machine learning, Modalities, ADOS, XG Boost, Precision, Accuracy, Confusion Matrix, F1 Score.

n[if 424 equals=”Regular Issue”][This article belongs to Journal of Instrumentation Technology & Innovations(joiti)]

n

[/if 424][if 424 equals=”Special Issue”][This article belongs to Special Issue under section in Journal of Instrumentation Technology & Innovations(joiti)][/if 424][if 424 equals=”Conference”]This article belongs to Conference [/if 424]

n

n

n

How to cite this article: Poonam Chaudhary, Anmol Bhatia, Vanshika Sangwan, Riya Saxena, Rahul, Sanyam Virmani. Early Autism Diagnosis: Machine Learning Models and Their Effectiveness. Journal of Instrumentation Technology & Innovations. August 14, 2024; ():-.

n

How to cite this URL: Poonam Chaudhary, Anmol Bhatia, Vanshika Sangwan, Riya Saxena, Rahul, Sanyam Virmani. Early Autism Diagnosis: Machine Learning Models and Their Effectiveness. Journal of Instrumentation Technology & Innovations. August 14, 2024; ():-. Available from: https://journals.stmjournals.com/joiti/article=August 14, 2024/view=0

nn[if 992 equals=”Open Access”] Full Text PDF Download[/if 992] n

n[if 992 not_equal=’Open Access’] [/if 992]n

nn

nn[if 379 not_equal=””]n

Browse Figures

n

n

[foreach 379]n

n[/foreach]n

n

n

n[/if 379]n

n

References

n[if 1104 equals=””]n

  1. Thabtah, F. (2019). Machine Learning in Autism Spectrum Disorder Behavioral Research: A Review and Ways Forward. Informatics, 6(1), 4.
  2. Bone, D., Bishop, S., Black, M. P., Goodwin, M. S., & Lord, C. (2016). Use of machine learning to improve autism screening and diagnostic instruments: effectiveness, efficiency, and multi-instrument fusion. Journal of Child Psychology and Psychiatry, 57(8), 927-937.
  3. Heinsfeld, A. S., Franco, A. R., Craddock, R. C., Buchweitz, A., & Meneguzzi, F. (2018). Identification of autism spectrum disorder using deep learning and the ABIDE dataset. NeuroImage: Clinical, 17, 16-23.
  4. Thabtah, F., Peebles, D., & Seker, H. (2020). A Machine Learning Autism Classification based on Logistic Regression Analysis. Health Information Science and Systems, 8(1), 1-9.
  5. van ’t Hof et al., “Age at autism spectrum disorder diagnosis: A systematic review and meta-analysis from 2012 to 2019,” Journal Title, vol. xx, no. xx, pp. xx-xx, Year.
  6. Duda, M., Ma, R., Haber, N., & Wall, D. P. (2016). Use of machine learning for behavioral distinction of autism and ADHD. Translational Psychiatry, 6(2), e732.
  7. L. Hyman et al., “Identification, Evaluation, and Management of Children With Autism Spectrum Disorder,” Pediatrics, vol. 145, no. 1, pp. e20193447, Jan. 2020.
  8. A. Shaw et al., “Early Identification of Autism Spectrum Disorder Among Children Aged 4 Years — Early Autism and Developmental Disabilities Monitoring Network, Six Sites, United States, 2016,” Surveillance Summaries, vol. 69, no. 3, pp. 1-11, Mar. 27, 2020.
  9. Li, M., Dai, Z., Wang, M., He, L., Huang, H., & Zheng, H. (2019). Predicting autism spectrum disorder based on multi-site MRI, clinical, and demographic data: a multisite machine learning study. Molecular Psychiatry, 24(10), 1599-1605.
  10. Lundberg, S. M., Erion, G. G., & Lee, S. I. (2020). Consistent Individualized Feature Attribution for Tree Ensembles. Nature Machine Intelligence, 2(5), 252-260.
  11. Chen, Q. Huang, L. Shi, Y. He, and Y. X. Li, “Associations between gut microbiota and autism spectrum disorder: a systematic review and meta-analysis,” PLoS ONE, vol. 14, no. 9, pp. e0222907, 2019.
  12. P. Chen, C. P., Keown, C. L., Jahedi, A., & Muller, R. A. (2015). Deep learning in autism diagnosis and biomarker discovery. Journal of Autism and Developmental Disorders, 45(4), 1123-1136.
  13. Guang et al., “Synaptopathology Involved in Autism Spectrum Disorder,” Journal Title, vol. xx, no. xx, pp. xx-xx, Year.
  14. Yechiam, O. Arshavsky, S. G. Shamay-Tsoory, S. Yaniv, and J. Aharon, “Adapted to explore: Reinforcement learning in Autistic Spectrum Conditions,” Journal Title, vol. xx, no. xx, pp. xx-xx, Year.
  15. Bi, X., Wang, Z., Yang, Y., Gao, Y., & Xu, Y. (2018). A multi-feature learning model for early autism spectrum disorder diagnosis based on structural and functional MRI. Frontiers in Neuroscience, 12, 707.
  16. Chen, Q., Chen, X., Zhang, Y., & Ma, Q. (2020). Deep learning for neuroimaging-based diagnosis and rehabilitation of autism spectrum disorder: a review. Frontiers in Neuroscience, 14, 1007.
  17. L. Hyman et al., “Identification, Evaluation, and Management of Children With Autism Spectrum Disorder,” Pediatrics, vol. 145, no. 1, pp. e20193447, Jan. 2020.
  18. Lord, E. H. Cook, B. L. Leventhal, and D. G. Amaral, “Autism Spectrum Disorders Review,” Pediatrics, vol. xx, no. xx, pp. xx-xx, Year.
  19. Abraham, A., Milham, M. P., Di Martino, A., Craddock, R. C., Samaras, D., Thirion, B., & Varoquaux, G. (2017). Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example. NeuroImage, 147, 736-745.
  20. Hazlett, H. C., Gu, H., Munsell, B. C., Kim, S. H., Styner, M., Wolff, J. J., & Piven, J. (2017). Early brain development in infants at high risk for autism spectrum disorder. Nature, 542(7641), 348-351.
  21. Khosla, M., Jamison, K., Ngo, G. H., Kuceyeski, A., & Sabuncu, M. R. (2019). Machine learning in resting-state fMRI analysis. Magnetic Resonance Imaging, 64, 101-121.
  22. Kuwabara, H., Lu, J., Lim, L., Irie, H., & Lopez, P. T. (2016). Machine learning approaches for predicting autism spectrum disorder diagnosis using facial imaging features. Journal of Child Psychology and Psychiatry, 57(8), 927-937.
  23. Vabalas, A., Gowen, E., Poliakoff, E., & Casson, A. J. (2019). Machine learning algorithm validation with a limited sample size. PLoS One, 14(11), e0224365.

nn[/if 1104][if 1104 not_equal=””]n

    [foreach 1102]n t

  1. [if 1106 equals=””], [/if 1106][if 1106 not_equal=””],[/if 1106]

    2. n[/foreach]

n[/if 1104]

nn

nn[if 1114 equals=”Yes”]n

n[/if 1114]

n

n

[if 424 not_equal=””][else]Ahead of Print[/if 424] Subscription Original Research

n

n

[if 2146 equals=”Yes”][/if 2146][if 2146 not_equal=”Yes”][/if 2146]n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n[if 1748 not_equal=””]

[else]

[/if 1748]n

n

n

Volume
[if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424]
Received June 9, 2024
Accepted June 28, 2024
Published August 14, 2024

n

n

242

n

n

n

n nfunction myFunction2() {nvar x = document.getElementById(“browsefigure”);nif (x.style.display === “block”) {nx.style.display = “none”;n}nelse { x.style.display = “Block”; }n}ndocument.querySelector(“.prevBtn”).addEventListener(“click”, () => {nchangeSlides(-1);n});ndocument.querySelector(“.nextBtn”).addEventListener(“click”, () => {nchangeSlides(1);n});nvar slideIndex = 1;nshowSlides(slideIndex);nfunction changeSlides(n) {nshowSlides((slideIndex += n));n}nfunction currentSlide(n) {nshowSlides((slideIndex = n));n}nfunction showSlides(n) {nvar i;nvar slides = document.getElementsByClassName(“Slide”);nvar dots = document.getElementsByClassName(“Navdot”);nif (n > slides.length) { slideIndex = 1; }nif (n (item.style.display = “none”));nArray.from(dots).forEach(nitem => (item.className = item.className.replace(” selected”, “”))n);nslides[slideIndex – 1].style.display = “block”;ndots[slideIndex – 1].className += ” selected”;n}n”}]