Normalization of Behavioral Features in Autism Spectrum Disorder with Influencing Attributes
K. Vijayalakshmi1, M. Vinayakamurthy2, Anuradha3
1K. Vijayalakshmi, Research Scholar, REVA University, Bangalore, India.
2Dr. M. Vinayakamurthy, Professor, REVA University, Bangalore, India.
3Dr. Anuradha, PG Department, STC College, Pollachi, Tamilnadu. India.

Manuscript received on 15 August 2019 | Revised Manuscript received on 21 August 2019 | Manuscript published on 30 August 2019 | PP: 2894-2897 | Volume-8 Issue-10, August 2019 | Retrieval Number: J96170881019/2019©BEIESP | DOI: 10.35940/ijitee.J9617.0881019
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Abstract: The brain developmental disorder that affects both behavior and communication is autism spectrum disorder (ASD) considered and recognized as a major medical issue affects the increasing population approximately 0.5%– 0.6%. It is a highly heterogeneous neuro-developmental condition that has severe symptoms with various comorbid disorders. Applied Behavior Analysis involves various methods to understand the change in behavior through therapy. The goal is to identify and increase relevant behaviors which can help to diagnose attributes that affect learning. Data mining as the technology handles such medical grounds to predict by analyzing patterns in huge data sets. The outline of the proposed work is to find the relevant attributes from the dataset by normalizing and ranking the attributes. The CFS subset evaluator using various search methods like best first, greedy stepwise and exhaustive search are used to filter relevant feature from the dataset. The ultimate objective of this paper work is to examine the ASD applied behaviors with subject to normalization and ranking. Applying these to the feature selection methods would help for better understanding on various currently wide spread complex medical condition.
Index Terms: Autism Spectrum Disorder (ASD), Applied Behavior Analysis (ABA), Data Mining, Feature Selection.

Scope of the Article: Data Mining