Prognostication of Autism Spectrum Disorder (ASD) using Supervised Machine Learning Models
N V Ganapathi Raju1, Karanam Madhavi2, G Sravan Kumar3, G Vijendar Reddy4, Kunaparaju Latha5, K Lakshmi Sushma6

1N.V. Ganapathi Raju, Department of  I.T., GRIET, Hyderabad (Telangana), India.
2Karanam Madhavi, Department of CSE, GRIET, Hyderabad (Telangana), India.
3G Sravan Kumar, Department of  I.T., GRIET, Hyderabad (Telangana), India.
4G Vijendar Reddy, Department of  I.T., GRIET, Hyderabad (Telangana), India.
5Kunaparaju Latha, Department of BS, GRIET, Hyderabad (Telangana), India.
6K L Sushma, Department of  I.T., GRIET, Hyderabad (Telangana), India.
Manuscript received on 18 April 2019 | Revised Manuscript received on 25 April 2019 | Manuscript published on 30 April 2019 | PP: 1028-1032 | Volume-8 Issue-4, April 2019 | Retrieval Number: D6547048419/19©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: autism spectrum disorder (ASD) screening is a psychiatric disorder which leads to neurological and developmental growth of a person which begins early in childhood and lasts throughout a person’s life. This disorder is caused by differences in the brain, genetics and environmental conditions. The disorder also includes limited and repetitive patterns of behaviour. It affects how a person interacts with others, communicates, and learns. The main areas of functioning affected in people with ASD qualitative impairments in social interaction and qualitative impairments in communication. Among the affected, it was observed that more men were affected with this disorder when compared to women. The cases related to these disorders are increasing progressively. The centres of disease control and prevention (CDC) currently estimate that one in 59 children is diagnosed with ASD disorder. Unfortunately, waiting time to diagnosis these disorders are lengthy and procedures are not cost effective. To overcome the time complexity for identifying the disorder, computational intelligence can be used by making use of advanced technologies such as machine learning to improve precision, accuracy and quality of the diagnosis process. Machine learning helps us by providing intelligent techniques to discover useful hidden (or) concealed patterns, which can be utilized in prediction and to improve decision making.
Keywords: Autism Spectrum Disorder, Disease Control And Prevention, Healthcare, Supervised Classification

Scope of the Article: Classification