Linear Discriminant Analysis Based Genetic Algorithm with Generalized Regression Neural Network – A Hybrid Expert System for Diagnosis of Diabetes

Abstract

Among the applications enabled by expert systems, disease diagnosis is a particularly important one. Nowadays, diabetes is found to be a complex health issue in human life. There has been a wide range of intelligent methods proposed for early detection of diabetes. The objective of this paper is to propose an expert system for better diagnosis of diabetes. The methodology of the proposed framework is classified as two stages: (a) Linear Discriminant Analysis (LDA) based genetic algorithm for feature selection, (b) Generalized Regression Neural Network (GRNN) for classification. The proposed a genetic algorithm with Linear Discriminant Analysis (LDA) based feature selection for not only reduce the computation time and cost of the disease diagnosis but also improved the accuracy of classification. The performance of the method is evaluated using the calculation of accuracy, confusion matrix and Receiver-Operating Characteristic (ROC). The proposed method is compared with other existing methods for evaluating the performance and accuracy. The LDA based Genetic Algorithm (GA) with GRNN produces the accuracy of 80.2017% with a ROC of 0.875.