@inproceedings{55604ad707a04c61a0fd83db07902266,
title = "Detection of Chronic Kidney Disease and Selecting Important Predictive Attributes",
abstract = "Chronic kidney disease (CKD) is a major public health concern with rising prevalence. In this study we consider 24 predictive parameters and create a machine learning classifier to detect CKD. We evaluate our approach on a dataset of 400 individuals, where 250 of them have CKD. Using our approach we achieve a detection accuracy of 0.993 according to the F1-measure with 0.1084 root mean square error. This is a 56% reduction of mean square error compared to the state of the art (i.e., the CKD-EPI equation: a glomerular filtration rate estimator). We also perform feature selection to determine the most relevant attributes for detecting CKD and rank them according to their predictability. We identify new predictive attributes which have not been used by any previous GFR estimator equations. Finally, we perform a cost-accuracy tradeoff analysis to identify a new CKD detection approach with high accuracy and low cost.",
keywords = "Chronic kidney disease, feature selection, machine learning",
author = "Asif Salekin and John Stankovic",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE International Conference on Healthcare Informatics, ICHI 2016 ; Conference date: 04-10-2016 Through 07-10-2016",
year = "2016",
month = dec,
day = "6",
doi = "10.1109/ICHI.2016.36",
language = "English (US)",
series = "Proceedings - 2016 IEEE International Conference on Healthcare Informatics, ICHI 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "262--270",
editor = "Wai-Tat Fu and Kai Zheng and Larry Hodges and Gregor Stiglic and Ann Blandford",
booktitle = "Proceedings - 2016 IEEE International Conference on Healthcare Informatics, ICHI 2016",
}