Decision tree and artificial immune systems for stroke prediction in imbalanced data
Created by W.Langdon from
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- @Article{SANTOS:2022:ESA,
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author = "Laercio Ives Santos and Murilo Osorio Camargos and
Marcos Flavio Silveira Vasconcelos D'Angelo and
Joao Batista Mendes and
Egydio Emiliano {Camargos de Medeiros} and Andre Luiz Sena Guimaraes and
Reinaldo Martinez Palhares",
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title = "Decision tree and artificial immune systems for stroke
prediction in imbalanced data",
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journal = "Expert Systems with Applications",
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year = "2022",
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volume = "191",
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pages = "116221",
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keywords = "genetic algorithms, genetic programming, Stroke
prediction, Artificial immune systems, Decision tree",
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ISSN = "0957-4174",
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URL = "https://www.sciencedirect.com/science/article/pii/S0957417421015347",
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DOI = "doi:10.1016/j.eswa.2021.116221",
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abstract = "Although cerebral stroke is a important public
worldwide health problem with more than 43 million
global cases reported recently, more than 90percent of
metabolic risk factors are controllable. Therefore,
early treatment can take advantage of a fast and
low-cost diagnosis to minimize the disease's sequels.
The use Machine Learning (ML) techniques can provide an
early and low-cost diagnosis. However, the performance
of these techniques is reduced in problems of
prediction of rare events and with class imbalance. We
proposed Machine learning approach to cerebral stroke
prediction based on Artificial Immune Systems (AIS) and
Decision Trees (DT) induced via Genetic Programming
(GP). In general, the approaches for stroke prediction
presented in the literature do not allow the
development of models considered interpretable; our
approach, on the other hand, uses a simplification
operator that reduces the complexity of the induced
trees to increase their interpretability. We evaluated
our approach on a highly imbalanced data set with only
1.89percent stroke cases and used AIS combined with One
Sided Selection (OSS) to create a new balanced data
set. This new data set is used by the GP to evolve a
population of DTs, and, at the end of this process, the
best tree is used to classify new instances. Two
experiments are used to test the proposed approach. In
the first experiment, our approach achieved, in terms
of sensitivity and specificity, are 70percent and
78percent, respectively, indicating its competitiveness
with the state-of-the-art technique. The second
experiment evaluates the proposed simplification
mechanism in creating rules that can be interpreted by
humans. The proposed approach can effectively increase
sensitivity and specificity while maintaining accurate
prediction using interpretable models, indicating its
potential to be clinically used in stroke diagnosis",
- }
Genetic Programming entries for
Laercio Ives Santos
Murilo Osorio Camargos
Marcos Flavio Silveira Vasconcelos D'Angelo
Joao Batista Mendes
Egydio Emiliano Camargos de Medeiros
Andre Luiz Sena Guimaraes
Reinaldo Martinez Palhares
Citations