International Journal of Advanced Networking and Applications

Open Access Open Access  Restricted Access Subscription Access

A Survey on Mining Algorithms for Predictive Analytics of Asd


 

Autism is a lifelong developmental deficit that affects how people perceive the world and interact with each others. An estimated one in more than 100 people has autism. The Autism disorder is most commonly seen in boys than girls. The commonly used tools to analyzing the dataset of autism are FMRI, EEG, and more recently "eye tracking". A several tests has been made on the ones who are suffering from Autism which helps to study on ASD and provide with the results to take further steps to treat them. In this research paper we study the algorithms which improves the prediction levels of Autism and provide a review on which algorithm is best suited to predict the autism in early stages. The data mining algorithms which used to predict the autism were mainly categorized in 4 types.

Keywords

Autism, FMRI, ECG, EYE Tracking, PCA, Data Mining.
User
Notifications
Font Size

  • J. Fan et al., "A Step Towards EEG-based Brain Computer Interface for Autism Intervention," presented at the 37th Annual International Conference of the IEEE Engineering in Medicine and Biological Society (EMBS), in press, 2015.

  • O. Grynszpan, J.-C. Martin, and J. Nadel, "Multimedia interfaces for users with high functioning autism: An empirical investigation," International Journal of Human-Computer Studies, vol. 66, no. 8, pp. 628-639, 2008.

  • D. L. Christensen, "Prevalence and characteristics of autism spectrum disorder among children aged 8 years—autism and developmental disabilities monitoring network, 11 sites, United States, 2012," MMWR. Surveillance Summaries, vol. 65, 2016.

  • M. Uljarevic and A. Hamilton, "Recognition of emotions in autism: a formal meta-analysis," Journal of autism and developmental disorders, vol. 43, no.7, pp. 1517-1526, 2013.

  • Nermend, Kesra. "Vector calculus in regional development analysis: Comparative regional analysis using the example of Poland". In Springer Science & Business Media, 2009.

  • Adankon, Mathias M., and Mohamed Cheriet. "Support vector machine." In Encyclopedia of biometrics, Springer US, 2009, pp 1303-1308.

  • Y. Liu, Z. Li, H. Xiong, X. Gao and J. Wu, "Understanding of internal clustering validation measures," in IEEE 10th International Conference on Data Mining (ICDM), 2010.

  • Frank E, Hall M, Holmes G, Kirkby R, Pfahringer B, Witten IH, Trigg L (2009) Weka-a machine learning workbench for data mining. In: Data mining and knowledge discovery handbook. Springer, Boston, pp 1269–1277.


Abstract Views: 201

PDF Views: 0




  • A Survey on Mining Algorithms for Predictive Analytics of Asd

Abstract Views: 201  |  PDF Views: 0

Authors

Abstract


Autism is a lifelong developmental deficit that affects how people perceive the world and interact with each others. An estimated one in more than 100 people has autism. The Autism disorder is most commonly seen in boys than girls. The commonly used tools to analyzing the dataset of autism are FMRI, EEG, and more recently "eye tracking". A several tests has been made on the ones who are suffering from Autism which helps to study on ASD and provide with the results to take further steps to treat them. In this research paper we study the algorithms which improves the prediction levels of Autism and provide a review on which algorithm is best suited to predict the autism in early stages. The data mining algorithms which used to predict the autism were mainly categorized in 4 types.

Keywords


Autism, FMRI, ECG, EYE Tracking, PCA, Data Mining.

References