An Integrated System for Initial Prediction for Autism Spectrum Disorders
K. Ramya1, K.M. Sai Kiran2, K. Anekh3, S. Sathiya Narayanan.4

1K. Ramya*, Assistant professor at SRM Institute of Science and Technology, Ramapuram, Chennai.
2K.M.Sai Kiran, Final Year student at SRM Institute of Science and technology, Ramapuram, Chennai.
3K. Anekh, Final Year student at SRM Institute of Science and Technology, Ramapuram, Chennai.
4Sathiya Narayanan, student at SRM Institute of Science and Technology, Ramapuram, Chennai.
Manuscript received on April 01, 2020. | Revised Manuscript Received on April 03, 2020. | Manuscript published on April 15, 2020. | PP: 83-85 | Volume-4 Issue-8, April 2020. | Retrieval Number: H0786044820//2020©BEIESP | DOI: 10.35940/ijmh.H0786.044820

Open Access | Ethics and Policies | Cite
© The Authors. Published By: 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: Autism Spectrum Disorder ASD specifies where lots of behavioral disabilities, such as social connections and lack of commutating, repetitions of same works, and only single areas are focused. While ASD is noticed in initial stage, some deficiencies and behavioral patterns may not be found as the indication unless they affect a person’s life in a particular way. An infrastructure to record, detect and label the behavioural patterns of children with autism spectrum disorder (ASD) has been developed. Autism Spectrum Disorder (ASD) effects the entire life of a person. The major symptoms of ASD are noticed as lack of social contact and communication, same pattern recurrence, predetermined interests and works. The critical issues is ASD identified at initial stage and age. This study, the grouping technique for ASD diagnosis was utilized in children aged 4-11 years. The Linear Discriminant Analysis (LDA) and KNN algorithms are used for categorizing.
Keywords: Autism Spectrum Disorder (ASD), Linear Discriminant Analysis (LDA), Feature Selection Techniques (FST)