Optimization of Machine Learning Techniques on Autism Spectrum Disorder with Swarm Intelligence Based Feature Selection
K. Vijayalakshmi1, M. Vinayakamurthy2, V. Anuradha3
1K. Vijayalakshmi, M.C.A Degree from University of Bharadhidasan, Trichy India,
2Dr. M. Vinayakamurthy, Professor in School of Computer Science and Applications, REVA Unviersity, Bangalore, Karnataka, India.
3Dr. Anuradha, HOD, PG in a Reputed Institution, (Tamil Nadu), India.
Manuscript received on 10 March 2019 | Revised Manuscript received on 18 March 2019 | Manuscript published on 30 July 2019 | PP: 6248-6251 | Volume-8 Issue-2, July 2019 | Retrieval Number: B3529078219/2019©BEIESP | DOI: 10.35940/ijrte.B3529.078219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper is a study on the various machine learning algorithms in order to perform ASD (Autism spectrum Disorder) as per the DSM-V standards. ASD occurs more frequently among children and in order to diagnose this with better accuracy, the study on binary firefly algorithm, a swarm intelligence-based wrapper feature selection algorithm is used to obtain best results with optimum feature subsets. This paper will provide overall result after applying it to all types of machine learning models on supervised learning.
Index Terms: ASD, Binary Firefly, Machine Learning, Swarm Intelligence.
Scope of the Article: Machine Learning