Evolutionary correlated gravitational search algorithm (ECGS) with genetic optimized Hopfield neural network (GHNN) – A hybrid expert system for diagnosis of diabetes. (October 2019)

• Record Type: Journal Article
• Authors: Jayashree, J.; Ananda Kumar, S.
• Is Part Of: Measurement. Volume 145(2019)
• Page Start: 551
• Page End: 558
• Publication Date: 2019-10
• Subjects: Diabetics -- Evolutionary correlated gravitational search algorithm (ECGS) -- Genetic optimized Hopfield neural network (GHNN) -- Pima Indian Diabetic Dataset; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2018.12.083
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: The evolutionary correlated gravitational search algorithm (ECGS) for selecting the optimized features. Analyzes each diabetic feature according to the correlation and mutual information is selected. The selected features are processed by GHNN for predicting the diabetic related features effectively. The efficiency of the system is implemented using MATLAB tool. It utilizes the Pima Indian Diabetic Dataset for analyzing the efficiency of introduced diabetic expert system. Abstract: In worldwide 415 million of peoples are affected by diabetics in the year of 2015, that is increased from the year of 2012. Based on the survey, it clearly shows the diabetics are one of the dangerous diseases because it leads to create several risk of early death. Due to the seriousness of the diabetic, it has been detected in early stage by creating expert system. During this process, the expert system has several issues such as accuracy of prediction due to the huge dimension of the diabetic feature that reduce the entire efficiency of the system. So, in this paper introduced the evolutionary correlated gravitational search algorithm (ECGS) for selecting the optimized features. The introduced method analyzes each diabetic feature according to the correlation and mutual information is selected with minimum computation time and cost. The selected features are processed by genetic optimized Hopfield neural network (GHNN) for predicting the diabetic related features effectively. Then the efficiency of the system is implemented using MATLAB tool that utilizes the Pima Indian Diabetic Dataset for analyzing the efficiency of introduced diabetic expert system. The efficiency of the system is evaluated in terms of using mean square error rate, F-measurer, accuracy, confusion matrix and ROC curve.