Comparison of different methods for determining diabetes

In this study, the Pima Indian Diabetes dataset was categorized with 8 different classifiers. The data were taken from the University of California Irvine Machine Learning Repository s web site. As a classifier, 6 different neural networks [probabilistic neural network (PNN), learning vector quantization, feedforward networks, cascade-forward networks, distributed time delay networks (DTDN), and time delay networks], the artificial immune system, and the Gini algorithm from decision trees were used. The classifier s performance ratios were studied separately as accuracy, sensitivity, and specificity and the success rates of all of the classifiers are presented. Among these 8 classifiers, the best accuracy and specificity values were achieved with the DTDN and the best sensitivity value was achieved with the PNN.