A novel fitness function in genetic programming for medical data classification
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- @Article{KUMAR:2020:JBI,
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author = "Arvind Kumar and Nishant Sinha and Arpit Bhardwaj",
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title = "A novel fitness function in genetic programming for
medical data classification",
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journal = "Journal of Biomedical Informatics",
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year = "2020",
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volume = "112",
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pages = "103623",
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month = dec,
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keywords = "genetic algorithms, genetic programming, Medical data
classification, Fitness function, Unbalanced data
classification",
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ISSN = "1532-0464",
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URL = "https://www.sciencedirect.com/science/article/pii/S1532046420302513",
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DOI = "doi:10.1016/j.jbi.2020.103623",
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abstract = "In the last decade, machine learning (ML) techniques
have been widely applied to identify different
diseases. This facilitates an early diagnosis and
increases the chance of survival. The majority of
medical data-sets are unbalanced. Due to this, ML
classification techniques give biased classification
over the majority class. In this paper, a novel fitness
function in Genetic Programming, for medical data
classification has been proposed that handles the
problem of unbalanced data. Four benchmark medical
data-sets named chronic kidney disease (CKD),
fertility, BUPA liver disorder, and Wisconsin
diagnostic breast cancer (WDBC) have been taken from
the University of California (UCI) machine learning
repository. Classification is done using the proposed
technique. The proposed technique achieved the best
accuracy for CKD, WDBC, Fertility, and BUPA dataset as
100percent, 99.12percent, 85.0percent, and 75.36percent
respectively, and the best AUC as 1.0, 0.99, 0.92, and
0.75 respectively. The result outcomes show an
improvement over other GP and SVM methods that confirm
the efficiency of our proposed algorithm",
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notes = "Also known as \cite{kumar2020novel}",
- }
Genetic Programming entries for
Arvind Kumar
Nishant Sinha
Arpit Bhardwaj
Citations