A hybrid algorithm of ML and XAI to prevent breast cancer: a strategy to support decision making

Silva-Aravena, Fabián; Núñez Delafuente, Hugo; Gutiérrez-Bahamondes, Jimmy H; Morales, Jenny

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dc.contributor.author	Silva-Aravena, Fabián
dc.contributor.author	Núñez Delafuente, Hugo
dc.contributor.author	Gutiérrez-Bahamondes, Jimmy H
dc.contributor.author	Morales, Jenny
dc.date.accessioned	2023-06-05T20:23:40Z
dc.date.available	2023-06-05T20:23:40Z
dc.date.issued	2023
dc.identifier.uri	http://repositorio.ucm.cl/handle/ucm/4819
dc.description.abstract	Worldwide, the coronavirus has intensified the management problems of health services, significantly harming patients. Some of the most affected processes have been cancer patients’ prevention, diagnosis, and treatment. Breast cancer is the most affected, with more than 20 million cases and at least 10 million deaths by 2020. Various studies have been carried out to support the management of this disease globally. This paper presents a decision support strategy for health teams based on machine learning (ML) tools and explainability algorithms (XAI). The main methodological contributions are: first, the evaluation of different ML algorithms that allow classifying patients with and without cancer from the available dataset; and second, an ML methodology mixed with an XAI algorithm, which makes it possible to predict the disease and interpret the variables and how they affect the health of patients. The results show that first, the XGBoost Algorithm has a better predictive capacity, with an accuracy of 0.813 for the train data and 0.81 for the test data; and second, with the SHAP algorithm, it is possible to know the relevant variables and their level of significance in the prediction, and to quantify the impact on the clinical condition of the patients, which will allow health teams to offer early and personalized alerts for each patient.	es_CL
dc.language.iso	en	es_CL
dc.rights	Atribución-NoComercial-SinDerivadas 3.0 Chile	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/cl/	*
dc.source	Cancers, 15(9), 2443	es_CL
dc.subject	Machine learning	es_CL
dc.subject	Explainable artificial intelligence	es_CL
dc.subject	Risk factors	es_CL
dc.subject	Breast cancer prevention	es_CL
dc.subject	Decision support systems	es_CL
dc.title	A hybrid algorithm of ML and XAI to prevent breast cancer: a strategy to support decision making	es_CL
dc.type	Article	es_CL
dc.ucm.facultad	Facultad de Ciencias Sociales y Económicas	es_CL
dc.ucm.indexacion	Scopus	es_CL
dc.ucm.indexacion	Isi	es_CL
dc.ucm.uri	mdpi.com/2072-6694/15/9/2443	es_CL
dc.ucm.doi	doi.org/10.3390/cancers15092443	es_CL