A comparative study on diabetes disease diagnosis using neural networks

Diabetes occurs when a body is unable to produce or respond properly to insulin which is needed to regulate glucose. Besides contributing to heart disease, diabetes also increases the risks of developing kidney disease, blindness, nerve damage, and blood vessel damage. Diabetes disease diagnosis via proper interpretation of the diabetes data is an important classification problem. In this study, a comparative pima-diabetes disease diagnosis was realized. For this purpose, a multilayer neural network structure which was trained by Levenberg–Marquardt (LM) algorithm and a probabilistic neural network structure were used. The results of the study were compared with the results of the pervious studies reported focusing on diabetes disease diagnosis and using the same UCI machine learning database.