Abstract
Missing data is a common problem in clinical datasets due to the large amount of information generated that must be handled, mostly in places where data is entered manually by staff or patients or when sensors or devices for data collection are faulty or damaged. In this work we compare different supervised learning algorithms with an incomplete chronic kidney disease dataset. The aim of this comparison is to select an algorithm to use with missing data from hypertensive patients. In this way, we want to be able to prevent or diagnose chronic kidney disease in hypertensive patients, while we are monitoring their lifestyle through a clinical process improvement based on personalised recommendations using multiple physiological and environmental variables.
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Acknowledgments
This work has been granted by the Ministerio de Economía y Competitividad of the Spanish Government (ref. TIN2014-53067-C3-1-R) and cofinanced by the European Regional Development Fund.
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Vives-Boix, V., Ruiz-Fernández, D., Marcos-Jorquera, D., Gilart-Iglesias, V. (2017). Impact of Missing Clinical Data for the Monitoring of Patients with Chronic Diseases. In: Ochoa, S., Singh, P., Bravo, J. (eds) Ubiquitous Computing and Ambient Intelligence. UCAmI 2017. Lecture Notes in Computer Science(), vol 10586. Springer, Cham. https://doi.org/10.1007/978-3-319-67585-5_39
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DOI: https://doi.org/10.1007/978-3-319-67585-5_39
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