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A Hybrid Approach for Enhancing the Classification Accuracy for Diabetes Disease

A Hybrid Approach for Enhancing the Classification Accuracy for Diabetes Disease

Maryam Mohammed Al-Nussairi, Mohammad Ali H. Eljinini
Copyright: © 2022 |Volume: 15 |Issue: 1 |Pages: 18
ISSN: 1938-7857|EISSN: 1938-7865|EISBN13: 9781683180340|DOI: 10.4018/JITR.298024
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MLA

Al-Nussairi, Maryam Mohammed, and Mohammad Ali H. Eljinini. "A Hybrid Approach for Enhancing the Classification Accuracy for Diabetes Disease." JITR vol.15, no.1 2022: pp.1-18. http://doi.org/10.4018/JITR.298024

APA

Al-Nussairi, M. M. & Eljinini, M. A. (2022). A Hybrid Approach for Enhancing the Classification Accuracy for Diabetes Disease. Journal of Information Technology Research (JITR), 15(1), 1-18. http://doi.org/10.4018/JITR.298024

Chicago

Al-Nussairi, Maryam Mohammed, and Mohammad Ali H. Eljinini. "A Hybrid Approach for Enhancing the Classification Accuracy for Diabetes Disease," Journal of Information Technology Research (JITR) 15, no.1: 1-18. http://doi.org/10.4018/JITR.298024

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Abstract

This paper proposes a new training algorithm for artificial neural networks based on an enhanced version of the grey wolf optimizer (GWO) algorithm. The proposed model is used for classifying the patients of diabetes disease. The results showed that the proposed training algorithm enhanced the performance of ANNs with a better classification accuracy as compared to the other state of art training algorithms for the classification of diabetes on publicly available “Pima Indian Diabetes (PID) dataset”. Several experiments have been executed on this dataset with variation in size of the population, techniques to handle missing data, and their impact on classification accuracy has been discussed. Finally, the results are compared with other nature-inspired algorithms trained ANN. EGWO attained better results in terms of classification accuracy than the other algorithms. The convergence curve proved that EGWO had balanced the local and global search abilities because it was faster to reach better positions than the original GWO.