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:1.8051

@Article{DANGELO:2023:future,

• author = "Gianni D'Angelo and David Della-Morte and Donatella Pastore and Giulia Donadel and Alessandro {De Stefano} and Francesco Palmieri",
• title = "Identifying patterns in multiple biomarkers to diagnose diabetic foot using an explainable genetic programming-based approach",
• journal = "Future Generation Computer Systems",
• year = "2023",
• volume = "140",
• pages = "138--150",
• month = mar,
• keywords = "genetic algorithms, genetic programming, Diabetic Foot, Explainable Artificial Intelligence (XAI), Interpretability, Explainability, Genetic programming (GP), Symbolic regression (SR), Machine Learning",
• ISSN = "0167-739X",
• URL = "https://www.sciencedirect.com/science/article/pii/S0167739X2200334X",
• DOI = "doi:10.1016/j.future.2022.10.019",
• size = "13 pages",
• 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",
• 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