Clinical risk assessment of chronic kidney disease patients using genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @Article{kumar2021clinical,
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author = "Arvind Kumar and Nishant Sinha and Arpit Bhardwaj and
Shivani Goel",
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title = "Clinical risk assessment of chronic kidney disease
patients using genetic programming",
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journal = "Computer Methods in Biomechanics and Biomedical
Engineering",
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year = "2022",
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volume = "25",
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number = "8",
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pages = "887--895",
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note = "Online since 02 Nov 2021",
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keywords = "genetic algorithms, genetic programming, chronic
kidney disease, CKD, clinical risk assessment,
imbalanced classification, fitness function",
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publisher = "Taylor \& Francis",
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DOI = "doi:10.1080/10255842.2021.1985476",
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size = "9 pages",
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abstract = "Chronic kidney disease (CKD) is one of the serious
health concerns in the twenty-first century. CKD
impacts over 37 million Americans. By applying machine
learning (ML) techniques to clinical data, CKD can be
diagnosed early. This early detection of CKD can
prevent numerous loss of life. In this work, clinical
data set of 400 patients, available on the UCI
repository, are taken. Unfortunately, this data set
does not have an equal distribution of CKD and Non-CKD
samples. This imbalanced nature of data highly
influences the learning capabilities of classifiers.
Genetic Programming (GP) is an ML technique based on
the evolution of species. GP with standard fitness
function, also impacted by this imbalanced nature of
data. A new Euclidean distance-based fitness function
in GP is proposed to handle this imbalanced nature of
the data set. To compare the robustness of the proposed
work, other classification techniques, K-nearest
neighborhood (KNN), KNN with particle swarm
optimization (PSO), and GP with the standard fitness
function, is also applied. For ten-fold
cross-validation, the KNN shows an accuracy of
83.54percent with an AUC value of 0.69, the PSO-KNN
shows an accuracy of 96.79percent with an AUC value of
0.94, and the GP, with the newly proposed fitness
function, supersedes KNN and PSO-KNN and shows the
accuracy of 99.33percent with an AUC value of 0.99.",
- }
Genetic Programming entries for
Arvind Kumar
Nishant Sinha
Arpit Bhardwaj
Shivani Goel
Citations