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Artificial Bee Colony and Deep Neural Network-Based Diagnostic Model for Improving the Prediction Accuracy of Diabetes

Artificial Bee Colony and Deep Neural Network-Based Diagnostic Model for Improving the Prediction Accuracy of Diabetes

Anand Kumar Srivastava, Yugal Kumar, Pradeep Kumar Singh
Copyright: © 2021 |Volume: 12 |Issue: 2 |Pages: 19
ISSN: 1947-315X|EISSN: 1947-3168|EISBN13: 9781799861553|DOI: 10.4018/IJEHMC.2021030102
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MLA

Srivastava, Anand Kumar, et al. "Artificial Bee Colony and Deep Neural Network-Based Diagnostic Model for Improving the Prediction Accuracy of Diabetes." IJEHMC vol.12, no.2 2021: pp.32-50. http://doi.org/10.4018/IJEHMC.2021030102

APA

Srivastava, A. K., Kumar, Y., & Singh, P. K. (2021). Artificial Bee Colony and Deep Neural Network-Based Diagnostic Model for Improving the Prediction Accuracy of Diabetes. International Journal of E-Health and Medical Communications (IJEHMC), 12(2), 32-50. http://doi.org/10.4018/IJEHMC.2021030102

Chicago

Srivastava, Anand Kumar, Yugal Kumar, and Pradeep Kumar Singh. "Artificial Bee Colony and Deep Neural Network-Based Diagnostic Model for Improving the Prediction Accuracy of Diabetes," International Journal of E-Health and Medical Communications (IJEHMC) 12, no.2: 32-50. http://doi.org/10.4018/IJEHMC.2021030102

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Abstract

A large number of machine learning approaches are implemented in healthcare field for effective diagnosis and prediction of different diseases. The aim of these machine learning approaches is to build automated diagnostic tool for helping the physician as well as monitor the health status of patients. These diagnostic tools are widely adopted in intensive care unit for life expectancy of patients. In this study, an effort is made to design an automated diagnostic model for the diagnosis and prediction of diabetes patients. The proposed diagnostic model is designed using artificial bee colony (ABC) algorithm and deep neural network (DNN) technique, called ABC-DNN-based diagnostic model. The ABC algorithm is applied to determine the relevant features for diabetes prediction and diagnosis while DNN technique is adopted for the prediction and diagnosis of diabetes affected patients. The performance of proposed diagnostic model is tested over Pima Indian Diabetes dataset and evaluated using accuracy, sensitivity, specificity, F-measure, Kappa, and area under curve (AUC) parameters. Further, 10-fold and 50-50% training-testing method are considered to assess the performance of proposed diagnostic model. The experimental results of proposed ABC-DNN model is compared with DNN technique and several existing diabetes studies. It is observed that proposed ABC-DNN model achieves 94.74% accuracy rate using 10-fold method.