Data Pre-processing and Neural Network Algorithms for Diagnosis of Type II Diabetes: A Survey
Raj Anand1, Vishnu Pratap Singh Kirar2, Kavita Burse3
1Raj Anand, Department of Computer Science, Oriental College of Technology, Bhopal, India.
2Vishnu Pratap Singh Kirar, Department of Electronics & Communication, Truba Institute of Engineering & Information Technology, Bhopal, India.
3Dr. Kavita Burse, Department of Electronics & Communication, Oriental College of Technology, Bhopal, India.
Manuscript received on September 20, 2012. | Revised Manuscript received on October 19, 2012. | Manuscript published on October 30, 2012. | PP: 49-52 | Volume-2 Issue-1, October 2012.  | Retrieval Number: A0731092112 /2012©BEIESP

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Abstract: Diagnosis of type II diabetes in early stages is very challenging task due to complex inter dependence on various factors. It requires the critical need to develop medical diagnostic support systems which can be helpful for the medical practitioners in the diagnostic process. Neural network techniques have been successfully applied to the diagnosis of many medical problems. In this survey we compare the various neural network techniques for the diagnosis of diabetes. The Pima Indian data set is used to study the classification accuracy of the neural network algorithms. The various data pre-processing techniques are surveyed to improve the predictive accuracy of the neural network algorithms. 
Keywords: Type II diabetes, Pima Indian data set, neural networks, data pre-processing.