DRN HYBRID MODEL FOR PREDICTING AUTISM USING RAPID MINER TOOL

Ramya Rajagopal, B.S.E. Zoraida B.S.E. Zoraida

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.

Keywords


Autism Spectrum Disorder (ASD); New DRN (Deep Learning, Random Forest, Naïve Bayes) Model; Stacking; Vote; Ada Boost; Bagging; Bayesian Boosting; Rapid Miner.

Full Text:

PDF

References


Michael Siller and Marian Sigman, “The Behaviors of Parents of Children with Autism Predict the Subsequent Development of Their Children’s Communication”, Journal of Autism and Developmental Disorders, Vol. 32, No. 2, April 2002.

Tony Charman, Simon Baron-Cohen, John Swettenham, Gillian Baird, Auriol Drew and Antony Cox, “Predicting language outcome in infants with autism and pervasive developmental disorder”, International Journal of Language & Communication Disorders, Vol.38, No.3, 2003.

G. Leroy, A. Irmscher, and M.H. Charlop-Christy, "Data Mining Techniques to Study Therapy Success with Autistic Children", 2006 International Conference on Data Mining, 26 - 29 June 2006, Monte Carlo Resort, Las Vegas, USA.

S. Wheelwright, S. Baron-Cohen, N. Goldenfeld, J. Delaney, D. Fine, R. Smith, L. Weil and A. Wakabayashi, “Predicting Autism Spectrum Quotient (AQ) from the Systemizing Quotient-Revised (SQ-R) and Empathy Quotient (EQ)”, 2006.

Sheena angra and Sachin ahuja, “Analysis of student’s data using rapid miner”, Journal of Today’s Ideas – Tomorrow’s Technologies, Vol. 4, No. 1, June 2016 pp. 49–58.

Brian P. Keane, Orna Rosenthal, Nicole H. Chun and Ladan Shams, “Audiovisual integration in high functioning adults with autism”, Research in Autism Spectrum Disorder for Elsevier journal, Volume. 4, Issue 2, April-June 2010.

A.Martin, V.Gayathri, G.Saranya, P.Gayathri and Dr.Prasanna Venkatesan, “A Hybrid Model For Bankruptcy Prediction Using Genetic Algorithm, Fuzzy C-Means and Mars”, International Journal on Soft Computing ( IJSC ), Vol.2, No.1, February 2011.

Tanaya Guha, Member, IEEE, Zhaojun Yang, Student Member, IEEE, Ruth B. Grossman, and Shrikanth S. Narayanan, Fellow, IEEE, “A Computational Study of Expressive Facial Dynamics in Children with Autism”, IEEE TRANSACTIONS ON AFFECTIVE COMPUTING, VOL. XX, NO. X, MARCH 2016.

Prof. Dr. Ahmed Hassan, Asistant Prof. Osama Abdo Mohamed, Prof. Dr. Ahmed Soufi Abou-Taleb, and Mr. Amr Hassan, “A Hybrid Feature Selection Approach Of Ensemble Multiple Filter Methods And Wrapper Method For Improving The Classification Accuracy Of Microarray Data Set”, IRACST - International Journal of Computer Science and Information Technology & Security (IJCSITS), ISSN: 2249-9555 Vol. 3, No.2, April 2013.

Niyati Gupta, Arushi Rawal, Dr. V.L. Narasimhan, and Savita Shiwani, “Accuracy, Sensitivity and Specificity Measurement of Various Classification Techniques on Healthcare Data”, IOSR-Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661, p- ISSN: 2278-8727Volume 11, Issue 5 (May. - Jun. 2013), PP 70-73.

E. M. Albornoz, L. D. Vignolo, C. E. Martínez & D. H. Milone, "Genetic Wrapper Approach for Automatic Diagnosis of Speech Disorders related to Autism" 14th IEEE International Symposium on Computational Intelligence and Informatics (CINTI), nov, 2013.

M.S. Mythili and A.R.Mohamed Shanavas, “A Novel Approach to Predict the Learning Skills of Autistic Children using SVM and Decision Tree”, International Journal of Computer Science and Information Technologies, Vol. 5 (6) , 2014, 7288-7291.

Vitthal Manekar and Kalyani Waghmare, “ Improving Accuracy of SVM Using Hybrid Cultural Algorithm”, Int.J.Computer Technology & Applications,Vol 5 (3),1194-1197.

Parvathi I and Siddharth Rautaray, “Survey on Data Mining Techniques for the Diagnosis of Diseases in Medical Domain”, International Journal of Computer Science and Information Technologies, Vol. 5 (1), 2014, 838-846.

Priyanka Juneja and Anshul Anand, “Analyses of Autistic Patients By using Interpretation Value Analysis”, International Journal of Computer Science and Mobile Computing, Vol.3 Issue.7, July- 2014, pg. 585-593.

Mohana E and Poonkuzhali.S, “Categorizing The Risk Level Of Autistic Children Using Data Mining Techniques”, International Journal of Advance Research In Science And Engineering IJARSE, Vol. No.4, Special Issue (01), April 2015.

Priti S. Patel and Dr. S.G. Desai, ”A Comparative Study on Data Mining Tools”, International Journal of Advanced Trends in Computer Science and Engineering, Vol.4(2), March - April 2.

Motaz M. H. Khorshid , Tarek H. M. Abou-El-Enien and Ghada M. A. Soliman, “Hybrid Classification Algorithms For Terrorism Prediction In Middle East And North Africa”, International Journal of Emerging Trends & Technology in Computer Science, Volume 4, Issue 3, May-June 2015.

Ionuț Taranu, “Data mining in healthcare: decision making and precision”, Database Systems Journal vol. VI, no. 4/2015.

Priyanka Sanjay Podutwar and Prof. Ms. R. R. Tuteja, “Enhancing technique for Predictive Grading of Childhood Autism using Soft Computing”, International Journal of Research In Science & Engineering, Special Issue: Techno-Xtreme 16.

Ashmeet Singh, R Sathyaraj, “ A Comparison Between Classification Algorithms on Different Datasets Methodologies using Rapidminer”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 5, Issue 5, May 2016.

“Vaccine Adverse Event Reporting System‟ (VAERS), https://vaers.hhs.gov/data/data.

“Rapid Miner tool”, http://rapidminer.com/




DOI: https://doi.org/10.26483/ijarcs.v8i8.4604

Refbacks

  • There are currently no refbacks.




Copyright (c) 2017 International Journal of Advanced Research in Computer Science