Impact of Preprocessing for Diagnosis of Diabetes Mellitus Using Artificial Neural Networks

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.