DRN HYBRID MODEL FOR PREDICTING AUTISM USING RAPID MINER TOOL

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Published: Oct 20, 2017
DOI: https://doi.org/10.26483/ijarcs.v8i8.4604
Keywords:
Autism Spectrum Disorder (ASD), New DRN (Deep Learning, Random Forest, Naïve Bayes) Model, Stacking, Vote, Ada Boost, Bagging, Bayesian Boosting, Rapid Miner.

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Ramya Rajagopal
Bharathidasan University
B.S.E. Zoraida B.S.E. Zoraida
Bharathidasan University

Abstract

Autism is wrong connection between cells in the human brain which cause abnormalities in the brain structure or function. Every human being with Autism Spectrum Disorder (ASD) has unique symptoms and abilities. Symptoms of ASD typically appear during the first three years of human life. Autism had been classified as three different types such as serve autism, moderate autism, and mild autism. Diagnosing ASD is based on ASD historical dataset because there is no blood or other medical test. With this in mind this paper focuses on developing new hybrid DRN model is created by combining three different models like Deep Learning, Random Forest, and Naïve Bayes (DRN) models. DRN hybrid model is implemented in Rapid Miner tool to find the Accuracy, Precision, recall, Classification error and Executed time. The result obtained shows DRN model is better when compared to the existing models like, Ada Boost, Bagging, Vote, Stacking and Bayesian Boosting models. Hence DRN hybrid can be used to predicting autism using the historical dataset.

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Vol. 8 No. 8 (2017): September-October
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Author Biographies

Ramya Rajagopal, Bharathidasan University

Computer Science

B.S.E. Zoraida B.S.E. Zoraida, Bharathidasan University

Department of Computer science

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