Affiliations: Department of Information System, University of Malaya, Kuala Lumpur, Malaysia | Faculty of Science, Department of Computer Science, Lamar University, TX, USA | Faculty of Computer Science and Information Technology, Department of Computer System and Technology, University of Malaya, Kuala Lumpur, Malaysia
Note: [] Corresponding author. Vimala Balakrishnan, Department of Information System, University of Malaya, 50603 Kuala Lumpur, Malaysia. Tel.: +60 3 7967 6377; Fax: +60 3 7957 9249; E-mail: [email protected]
Abstract: Retinopathy or blindness due to diabetes is one of the most common complications among diabetics worldwide. Due to its high prevalence, early detections are necessary so as to avoid vision loss. This paper aims to discuss the design and development of a retinopathy predictive system which is based on data mining and case based reasoning (CBR). To be specific, C5.0 was used to produce the decision tree whereas k-nearest neighbour and Hamming distance algorithms were used to select the three most similar cases for every new case entered into the system. Then a voting mechanism makes the final prediction. Results show that the hybrid system has a better accuracy prediction rate (85%) compared to C5.0 (76%) and CBR (73%) implemented solely.
Keywords: Data mining, case based reasoning, C5.0, K-nearest neighbor
