Advertisement

DataAutism: An Early Detection Framework of Autism in Infants using Data Science

  • Venkatesh Gauri ShankarEmail author
  • Dilip Singh Sisodia
  • Preeti Chandrakar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1016)

Abstract

Data Science with analytics and machine learning in the field of health care are the most prominent and emerging fields in today’s scenario. Our research paper aims to the healthcare solution toward autism in infants. Autism is the neurodevelopment disorder categorized by diminished societal interaction, lingual and non-lingual communication, repetitive and antagonistic behavior. Autism neurodevelopment figures out in the infants nearly about one year of age. The overall process of autism detection is a very long and cost-oriented process that takes 6 months to 10 months in total. We are concentrating on two data set and developed a framework for early detection of autism in infants. Form the same above, we use the concept of data analytics with training of data model and inclusion of SVM classification. We have tested our model and novel algorithm “DataAutism” over large data set and figure out high precision, recall with accuracy approx. 89%.

Keywords

Data analytics Autism Classification Data science Support vector machine 

Notes

Acknowledgements

It is my privilege to express my sincere gratitude to National Institute of Technology, Raipur and Manipal University Jaipur for providing research platform and support to carry out research. We are thankful to National Institute of Mental Health and University of California, Irvine for making data available.

References

  1. 1.
    HealthLine autism classification. https://www.healthline.com/health/autism. Accessed July 22, 2018.
  2. 2.
    AutismSpeaks about autisms. https://www.autismspeaks.org/what-autism. Accessed August 02, 2018.
  3. 3.
    Healthitanalytics autism\(\_\)types. https://healthitanalytics.com/news/ehr-data-analytics-reveal-subtypes-of-autism-in-children. Accessed August 14, 2018.
  4. 4.
    Tang, J., Liu, J., Zhang, M., & Mei, Q. (2016). Visualizing large-scale and high-dimensional data. In Proceedings of the 25th International Conference on WWW (pp. 287–297).Google Scholar
  5. 5.
    Brinker, K. (2003). Incorporating diversity in active learning with support vector machines. In Proceedings of the 20th International Conference on Machine Learning (pp. 59–66). Washington, USA: ACM.Google Scholar
  6. 6.
    Devi, B., Kumar, S., & Anuradha, S. V. G. (2019). AnaData: A novel approach for data analytics using random forest tree and SVM. In B. Iyer, S. Nalbalwar, & N. Pathak (Eds.), Computing, communication and signal processing. Advances in intelligent systems and computing (Vol. 810). Singapore: Springer.  https://doi.org/10.1007/978-981-13-1513-8_53.Google Scholar
  7. 7.
    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.  https://doi.org/10.1016/j.nicl.2017.08.017.CrossRefGoogle Scholar
  8. 8.
    Abraham, A., Milham, M. P., Di Martino, A., Cameron Craddock, R., Samaras, D., Thirion, B., Varoquaux, G. (2017). Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example. NeuroImage, 147, 736–745, ISSN 1053-8119.  https://doi.org/10.1016/j.neuroimage.2016.10.045.CrossRefGoogle Scholar
  9. 9.
    Duda, M., Kosmicki, J. A., & Wall, D. P. (2014). Testing the accuracy of an observation-based classifier for rapid detection of autism risk. Translational Psychiatry, 4, e440.  https://doi.org/10.1038/tp.2014.65.CrossRefGoogle Scholar
  10. 10.
    Shankar, V. G., Devi, B., & Srivastava, S. DataSpeak: Data extraction, aggregation, and classification using big data novel algorithm. In B. Iyer, S. Nalbalwar, & N. Pathak (Eds.), Computing, communication and signal processing. Advances in intelligent systems and computing (Vol. 810). Singapore: Springer.  https://doi.org/10.1007/978-981-13-1513-8_16.Google Scholar
  11. 11.
    Wall, D. P., Dally, R., Luyster, R., Jung, J.-Y., & DeLuca, T. F. (2012). Use of artificial intelligence to shorten the behavioral diagnosis of autism. PLoS ONE, 7(8), art. no. e43855.Google Scholar
  12. 12.
    Jamal, W., Das, S., Maharatna, K., Kuyucu, D., Sicca, F., Billeci, L., Apicella, F., & Muratori, F. (2013). Using brain connectivity measure of EEG synchrostates for discriminating typical and autism spectrum disorder. In 2013 6th International IEEE/EMBS Conference (pp. 1402–1405), San Diego, CA.  https://doi.org/10.1109/NER.2013.6696205.
  13. 13.
    Shankar, V. G., Jangid, M., Devi, B., Kabra, S. (2018). Mobile big data: Malware and its analysis. In Proceedings of First International Conference on Smart System, Innovations and Computing. Smart Innovation, Systems and Technologies (Vol. 79, pp. 831–842). Singapore: Springer. https://doi.org/10.1007/978-981-10-5828-8_79.CrossRefGoogle Scholar
  14. 14.
    Di Martino, A., Yan, C.-G., & Milham, M. P. (2014). The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular Psychiatry, 19, 659–667.  https://doi.org/10.1038/mp.2013.78.CrossRefGoogle Scholar
  15. 15.
    UCI UCI Data Set 1. http://archive.ics.uci.edu/ml/machine-learning-databases/00419/. Accessed September 04, 2018.
  16. 16.
    UCI UCI Data Set 2. http://archive.ics.uci.edu/ml/machine-learning-databases/00420/. Accessed June 28, 2018.
  17. 17.
    CDC CDC Dataset. https://www.cdc.gov/ncbddd/autism/data.html. June 21, 2018.
  18. 18.
    NIMH NDAR Dataset. https://ndar.nih.gov/edit_collection.html?QA=false&id=1880. Accessed July 13, 2018.

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Venkatesh Gauri Shankar
    • 1
    • 2
    Email author
  • Dilip Singh Sisodia
    • 2
  • Preeti Chandrakar
    • 2
  1. 1.Manipal University JaipurJaipurIndia
  2. 2.National Institute of TechnologyRaipurIndia

Personalised recommendations