Abstract
Diabetic retinopathy is an ocular disease that involves an important healthcare spending and is the most serious cause of secondary blindness. Precocious and precautionary detection through a yearly screening of the eye fundus is difficult to make because of the large number of diabetic patients. This paper presents a novel clinical decision support system, based on fuzzy rules, that calculates the risk of developing diabetic retinopathy. The system has been trained and validated on a dataset of patients from Sant Joan de Reus University Hospital. The system achieves levels of sensitivity and specificity above 80 %, which is in practice the minimum threshold required for the validity of clinical tests.
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Acknowledgements
This study was funded by the Spanish research projects PI12/01535 and PI15-/01150 (Instituto de Salud Carlos III) and the URV grants 2014PFR-URV-B2-60 and 2015PFR-URV-B2-60.
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Saleh, E., Valls, A., Moreno, A., Romero-Aroca, P., de la Riva-Fernandez, S., Sagarra-Alamo, R. (2016). Diabetic Retinopathy Risk Estimation Using Fuzzy Rules on Electronic Health Record Data. In: Torra, V., Narukawa, Y., Navarro-Arribas, G., Yañez, C. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2016. Lecture Notes in Computer Science(), vol 9880. Springer, Cham. https://doi.org/10.1007/978-3-319-45656-0_22
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