Effective Autism Spectrum Disorder Prediction to Improve the Clinical Traits using Machine Learning Techniques

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2022 by IJETT Journal
Volume-70 Issue-4
Year of Publication : 2022
Authors : Dr. R. Surendiran, Dr. M. Thangamani, C. Narmatha, M. Iswarya
  10.14445/22315381/IJETT-V70I4P230

MLA 

MLA Style: Dr. Surendiran, R., et al. "Effective Autism Spectrum Disorder Prediction to Improve the Clinical Traits using Machine Learning Techniques ." International Journal of Engineering Trends and Technology, vol. 70, no. 4, Apr. 2022, pp. 343-359. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I4P230

APA Style: Dr. Surendiran, R., Dr. Thangamani, M., Narmatha, C., Iswarya, M..(2022). Effective Autism Spectrum Disorder Prediction to Improve the Clinical Traits using Machine Learning Techniques . International Journal of Engineering Trends and Technology, 70(4), 343-359. https://doi.org/10.14445/22315381/IJETT-V70I4P230

Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental complaint that influences an individual’s communication, announcement, and knowledge talents. Analysis of Autism can be completed at any age-group level. Autism patients look at diverse kinds of disputes learning disabilities, and complexity with meditation. Mental health problems, motor difficulties, and sensory problems are some of the problems faced by Autism patients. Earlier diagnosis and proper medication at the early stage are essential to control ASD. The ASD prediction framework is built to support a behavioral aspect-based analysis model without any device in this research. The ASD prediction process is focused on the childhood and adolescent analysis model utilized in the system. The behavioral parameters are collected with the support of the Autism Query collections. The decision tree (DT) and Support Vector Machine (SVM) techniques, K-Nearest Neighbors (KNN), and Artificial Neural Network (ANN) are applied for the ASD prediction process. The Correlated Feature selection based Random Forest (CFS-RT) algorithm is applied for the ASD prediction process, giving an accuracy of 93.03%, and ANN produces 97.68% and outperformance other methods.

Keywords
Autism Spectrum Disorder, Decision Tree, Machine Learning, Data Mining, Support Vector Machine.

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