Identifying patterns in multiple biomarkers to diagnose diabetic foot using an explainable genetic programming-based approach
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{DANGELO:2023:future,
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author = "Gianni D'Angelo and David Della-Morte and
Donatella Pastore and Giulia Donadel and
Alessandro {De Stefano} and Francesco Palmieri",
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title = "Identifying patterns in multiple biomarkers to
diagnose diabetic foot using an explainable genetic
programming-based approach",
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journal = "Future Generation Computer Systems",
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year = "2023",
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volume = "140",
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pages = "138--150",
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month = mar,
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keywords = "genetic algorithms, genetic programming, Diabetic
Foot, Explainable Artificial Intelligence (XAI),
Interpretability, Explainability, Genetic programming
(GP), Symbolic regression (SR), Machine Learning",
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ISSN = "0167-739X",
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URL = "https://www.sciencedirect.com/science/article/pii/S0167739X2200334X",
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DOI = "doi:10.1016/j.future.2022.10.019",
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size = "13 pages",
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abstract = "Diabetes mellitus is a global health problem,
recognised as the seventh cause of death in the world.
One of the most debilitating complications of diabetes
mellitus is the diabetic foot (DF), resulting in an
increased risk of hospitalization and significant
morbidity and mortality. Amputation above or below the
knee is a feared complication and the mortality in
these patients is higher than for most forms of cancer.
Identifying and interpreting relationships existing
among the factors involved in DF diagnosis is still
challenging. Although machine learning approaches have
proven to achieve great accuracy in DF prediction, few
advances have been performed in understanding how they
make such predictions, resulting in mistrust of their
use in real contexts. In this study, we present an
approach based on Genetic Programming to build a simple
global explainable classifier, named X-GPC, which,
unlike existing tools such as LIME and SHAP, provides a
global interpretation of the DFU diagnosis through a
mathematical model. Also, an easy consultable 3d graph
is provided, which could be used by the medical staff
to figure out the patients' situation and take
decisions for patients' healing. Experimental results
obtained by using a real-world dataset have shown the
ability of the proposal to diagnose DF with an accuracy
of 100percent outperforming other techniques of the
state-of-the-art",
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notes = "Department of Computer Science, University of Salerno,
Fisciano (SA), Italy",
- }
Genetic Programming entries for
Gianni D'Angelo
David Della-Morte
Donatella Pastore
Giulia Donadel
Alessandro De Stefano
Francesco Palmieri
Citations