Abstract
The purpose of this study is to analyse the Oporto Hospital Center (CHP) corneal transplantation process using Data Mining (DM) techniques, following the Cross Industry Standard Process for Data Mining (CRISP- DM) methodology. The DM goals focused on the definition and evaluation of DM models capable of predicting the priority of a request for a surgical procedure and its waiting time. Thus, 320 models were generated using the Pervasive Data Mining Engine (PDME) tool. The model results showed that although there is no model capable of effectively predicting all priority target classes, a “normal” class can be used to accurately perform this type of prediction, due to good sensitivity results. In some models, the sensitivity achieved results of 94% or even 99% along with an accuracy slightly over 80% for a specific target class.
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Acknowledgement
This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013. This work is also supported by the Deus ex Machina (DEM): Symbiotic technology for societal efficiency gains - NORTE-01-0145-FEDER-000026.
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Carneiro, B., Peixoto, R., Portela, F., Santos, M.F. (2017). Clinical Intelligence: A Data Mining Study on Corneal Transplantation. In: Ghosh, A., Pal, R., Prasath, R. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2017. Lecture Notes in Computer Science(), vol 10682. Springer, Cham. https://doi.org/10.1007/978-3-319-71928-3_10
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DOI: https://doi.org/10.1007/978-3-319-71928-3_10
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