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IJSTR >> Volume 4 - Issue 3, March 2015 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Prediction Of Diabetes Using Soft Computing Techniques- A Survey

[Full Text]

 

AUTHOR(S)

M. Durairaj, G. Kalaiselvi

 

KEYWORDS

Index Terms: Artificial Neural Network (ANN), C4.5 Classifier, Support Vector Machine (SVM), K-Nearest Neighbour (KNN).

 

ABSTRACT

Abstract: Neural Networks are one of the soft computing techniques that can be used to make predictions on medical data. Neural Networks are known as the Universal predictors. Diabetes mellitus or simply diabetes is a disease caused due to the increase level of blood glucose. Various traditional methods, based on physical and chemical tests, are available for diagnosing diabetes. The Artificial Neural Networks (ANNs) based system can effectively applied for high blood pressure risk prediction. This improved model separates the dataset into either one of the two groups. The earlier detection using soft computing techniques help the physicians to reduce the probability of getting severe of the disease. The data set chosen for classification and experimental simulation is based on Pima Indian Diabetic Set from (UCI) Repository of Machine Learning databases. In this paper, a detailed survey is conducted on the application of different soft computing techniques for the prediction of diabetes. This survey is aimed to identify and propose an effective technique for earlier prediction of the disease.

 

REFERENCES

[1] Asha Gowda Karegowda ,A.S. Manjunath , M.A. Jayaram,”Application Of Genetic Algorithm Optimized Neural Network Connection Weights For Medical Diagnosis Of Pima Indians Diabetes,” International Journal on Soft Computing ( IJSC ), Vol.2, No.2, May 2011.

[2] Ravi Sanakal, Smt. T Jayakumari, “Prognosis of Diabetes Using Data mining Approach-Fuzzy C Means Clustering and Support Vector Machine,” International Journal of Computer Trends and Technology (IJCTT) – volume 11 number 2 May 2014.

[3] K. Rajesh, V. Sangeetha, “Application of Data Mining Methods and Techniques for Diabetes Diagnosis,” International Journal of Engineering and Innovative Technology (IJEIT) Volume 2, Issue 3 September 2012.

[4] P. Radha, Dr. B. Srinivasan, “Predicting Diabetes by cosequencing the various Data Mining Classification Techniques,” IJISET - International Journal of Innovative Science, Engineering & Technology, Vol. 1 Issue 6, August 2014.

[5] Raj Anand, Vishnu Pratap Singh Kirar, Kavita Burse, “K-Fold Cross Validation and Classification Accuracy of PIMA Indian Diabetes Data Set Using Higher Order Neural Network and PCA,” International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-2, Issue-6, January 2013.

[6] Veena Vijayan V. Aswathy Ravikumar, “Study of Data Mining Algorithms for Prediction and Diagnosis of Diabetes Mellitus,”International Journal of Computer Applications (0975 – 8887) Volume 95– No.17, June 2014 .
[7] S.Priya R.R.Rajalaxmi,” An Improved Data Mining Model to Predict the Occurrence of Type-2 Diabetes using Neural Network,” International Journal of Computer Applications (0975 – 8887) Volume 95– No.17, 2012.

[8] Blanca S.Leona, AlmaY.Alanisb,n, EdgarN.Sancheza, Fernando Ornelas-Tellezc, EduardoRuiz-Velazquezb, “Inverse optimal neural control of blood glucose level for type1diabetes mellitus patients,” Journal of the Franklin Institute 349 (2012) 1851–1870.

[9] Paul S. Heckerling, Gay J. Canaris, Stephen , Flach, Thomas G. Tape,Robert S. Wigton, Ben S. Gerber, “Predictors of urinary tract infection based on artificial neural networks and genetic algorithms,” international journal of medical informatics 7 6, 2007.

[10] Sebastian Polak Aleksander Mendyk, “Artificial neural networks based Internet hypertension prediction tool development and validation,” Applied Soft Computing 8 (2008) 734–739.

[11] Pankaj Srivastava, Neeraj Sharma,Richa Singh, “Soft Computing Diagnostic System for Diabetes,” International Journal of Computer Applications (0975 – 888)Volume 47– No.18, June 2012.

[12] V.Karthikeyani, I.Parvin Begum, K.Tajudin, I.Shahina Begam, “Comparative of Data Mining Classification Algorithm (CDMCA) in Diabetes Disease Prediction,” International Journal on Computer Science and Engineering (IJCSE), December 2012.

[13] M. Durairaj, V. Ranjani, “Data Mining Applications In Healthcare Sector: A Study,” international journal of scientific & technology research volume 2, issue 10, October 2013.