A Novel Classification Method for Diagnosis of Diabetes Mellitus Using Artificial Neural Networks

被引：38
|
作者：
Jayalakshmi, T. [1 ]
Santhakumaran, A. [2 ]
机构：
[1] CMS Coll Sci & Commerce, Dept Comp Sci, Coimbatore, Tamil Nadu, India
[2] Salem Sowdeswari Coll, Dept Stat, Salem, India
来源：
PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA STORAGE AND DATA ENGINEERING (DSDE 2010) | 2010年
关键词：
Artificial Neural Networks; Diabetes Mellitus; Missing Value Analysis; Pre-Processing Methods;
D O I：
10.1109/DSDE.2010.58
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Many real world problems can be solved with Artificial Neural Networks in the areas of pattern recognition, signal processing and medical diagnosis. Most of the medical data set is seldom complete. Artificial Neural Networks require complete set of data for an accurate classification. This paper dwells on the various missing value techniques to improve the classification accuracy. The proposed system also investigates the impact on preprocessing during the classification. A classifier was applied to Pima Indian Diabetes Dataset and the results were improved tremendously when using certain combination of preprocessing techniques. The experimental system achieves an excellent classification accuracy of 99% which is best than before.
引用
收藏
页码：159 / 163
页数：5
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