Abstract
Diabetic Retinopathy (DR) is a threatening disease which causes blindness in diabetic patients. With the increasing number of DR cases, diabetic eye screening is a challenging task for experts. Adopting machine learning to create a high accuracy classifier will be able to reduce the burden of diabetic eye screening. Therefore, this paper aims to propose a high accuracy DR classifier using clinical attributes. This study was conducted using nine clinical attributes of 385 diabetic patients, who were already labelled regarding DR, where 79 patients did not suffer from DR (NODR), 161 patients had nonproliferative DR (NPDR), and 145 patients had proliferative DR (PDR). The data was then used to develop a DR classifier through the hybrid of optimised Support Vector Machine (SVM) and Artificial Neural Network (ANN). The experiment results showed that the hybrid classifier had a high accuracy of 94.55. The accuracy yield was higher compared to single classifier.
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Acknowledgement
The authors would like to thank Universiti Sains Malaysia for the assistance it has provided through the Fundamental Research Grant Scheme (203/PKOMP/6711802) to complete the current work.
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Ab Kader, N.I., Yusof, U.K., Sabudin, M. (2021). Hybridisation of Optimised Support Vector Machine and Artificial Neural Network for Diabetic Retinopathy Classification. In: Saeed, F., Mohammed, F., Al-Nahari, A. (eds) Innovative Systems for Intelligent Health Informatics. IRICT 2020. Lecture Notes on Data Engineering and Communications Technologies, vol 72. Springer, Cham. https://doi.org/10.1007/978-3-030-70713-2_9
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