Abstract
This paper addresses the applicability of a novel neural network model on early detection of Glaucoma disease. In this work, we propose to incorporate a especial case of Random Vector Functional Link (RVFL) into Rough neural architectures that consist of random weights rough (RW-Rough) neurons to handle the existing uncertainty in medical datasets. Moreover, for accurate and efficient parameter selection, a combination of the Biased Random Key Genetic Algorithm and random-weights neural networks is proposed for the model’s training. To evaluate the proposed method, the medical records of 500 normal and 400 glaucomatous persons have been collected from Labbafinezhad medical center, Tehran, Iran. Experimental results on the collected dataset show the superiority of the proposed model in comparison with recent data-driven algorithms in terms of sensitivity, specificity, and accuracy.
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Notes
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Pattern Standard Deviation.
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Mean Deviation.
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Saffari, M., Khodayar, M., Teshnehlab, M. (2022). Random Weights Rough Neural Network for Glaucoma Diagnosis. In: Xie, Q., Zhao, L., Li, K., Yadav, A., Wang, L. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 89. Springer, Cham. https://doi.org/10.1007/978-3-030-89698-0_55
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