DocumentCode :
2313944
Title :
Impact of Preprocessing for Diagnosis of Diabetes Mellitus Using Artificial Neural Networks
Author :
Jayalskshmi, T. ; Santhakumaran, A.
Author_Institution :
Comput. Sci. Dept., CMS Coll. of Sci. & Commerce, Coimbatore, India
fYear :
2010
fDate :
9-11 Feb. 2010
Firstpage :
109
Lastpage :
112
Abstract :
Medicine has always benefited from the technology. Artificial Neural Networks is currently the promising area of interest to solve medical problems. Diagnosis of diabetes is one of the most challenging problems in machine learning. This medical data set is seldom complete. Artificial neural networks require complete set of data for an accurate classification. The system explains how the pre-processing procedure and missing values influence the data set during the classification. The implemented system compares various missing value techniques and pre-processing techniques. Some combinations prove the real influence of these techniques. A classifier has applied to Pima Indian Diabetes dataset and the results were improved tremendously when using certain combination of preprocessing and missing value techniques. The experimental system achieves an excellent classification accuracy of 99% which is best than before.
Keywords :
diseases; learning (artificial intelligence); medicine; neural nets; patient diagnosis; pattern classification; set theory; Diabetes Mellitus; Pima Indian Diabetes dataset; artificial neural networks; classification; data set; diagnosis preprocessing; machine learning; medicine; Artificial neural networks; Blood; Diabetes; Educational institutions; Insulin; Machine learning; Medical diagnostic imaging; Neural networks; Noise level; Sugar; Artificial Neural Networks; Back Propagation Method; Diabetes Mellitus; Missing Value Analysis; Pre Processing Methods;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Computing (ICMLC), 2010 Second International Conference on
Conference_Location :
Bangalore
Print_ISBN :
978-1-4244-6006-9
Electronic_ISBN :
978-1-4244-6007-6
Type :
conf
DOI :
10.1109/ICMLC.2010.65
Filename :
5460760
Link To Document :
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