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Predictions Using Data Mining and Case-based Reasoning: A Case Study for Retinopathy
Abstract:Diabetes is one of the high prevalence diseases worldwide with increased number of complications, with retinopathy as one of the most common one. This paper describes how data mining and case-based reasoning were integrated to predict retinopathy prevalence among diabetes patients in Malaysia. The knowledge base required was built after literature reviews and interviews with medical experts. A total of 140 diabetes patients- data were used to train the prediction system. A voting mechanism selects the best prediction results from the two techniques used. It has been successfully proven that both data mining and case-based reasoning can be used for retinopathy prediction with an improved accuracy of 85%.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1056068Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2580
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