Exploratory Data Analysis and Decision Tree Modeling for Autism Spectrum Disorder: Machine Learning Approach

Authors

  • R. S. Kamath Department of Computer Studies, Chhatrapati Shahu Institute of Business Education and Research, Kolhapur Maharashtra, India
  • S. S. Jamsandekar Department of Computer Studies, Chhatrapati Shahu Institute of Business Education and Research, Kolhapur Maharashtra, India
  • V. L. Badadare Department of Computer Studies, Chhatrapati Shahu Institute of Business Education and Research, Kolhapur Maharashtra, India
  • R. K. Kamat Department of Electronics, Shivaji University, Kolhapur Maharashtra, India

DOI:

https://doi.org/10.21276/apjhs.2022.9.4S.31

Keywords:

Autism spectrum disorder, Decision tree, Exploratory data analysis, Machine learning, Recursive partitioning

Abstract

As the early diagnosis of autism spectrum disorder (ASD) is critical the high accuracy machine learning can be applied to achieve technology based diagnosis. In this context, the present study demonstrates machine learning approach for ASD diagnosis using decision tree (DT) modeling. The dataset employed in the present study comprises two classes of ASD adults with a sample size of 704 instances. The DT model entails a recursive partitioning approach implemented in the “rpart” package of R. The optimum model is derived by tuning parameters such as Min split, Min bucket, Max depth, and complexity. The performance of the model is evaluated in terms of the mean square error estimate of the error rate.

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Published

2022-06-25

How to Cite

R. S. Kamath, S. S. Jamsandekar, V. L. Badadare, & R. K. Kamat. (2022). Exploratory Data Analysis and Decision Tree Modeling for Autism Spectrum Disorder: Machine Learning Approach. Asian Pacific Journal of Health Sciences, 9(4), 161–164. https://doi.org/10.21276/apjhs.2022.9.4S.31

Issue

Vol. 9 No. 4 (2022): Vol 9 Issue 4 October-December(S) 2022

Section

Articles

License

Copyright (c) 2022 R. S. Kamath, S. S. Jamsandekar, V. L. Badadare, R. K. Kamat

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Asian Pacific Journal of Health Sciences applies the Creative Commons Attribution (CC-BY) license to published articles. Under this license, authors retain ownership of the copyright for their content, but they allow anyone to download, reuse, reprint, modify, distribute and/or copy the content as long as the original authors and source are cited. Appropriate attribution can be provided by simply citing the original article.