{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:27:11Z","timestamp":1760239631112,"version":"build-2065373602"},"reference-count":64,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2020,11,27]],"date-time":"2020-11-27T00:00:00Z","timestamp":1606435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"This research was funded by MINISTERIO DE CIENCIA E INNOVACI\u00d3N, Project: La desigualdad econ\u00f3mica en la Espa\u00f1a contempor\u00e1nea y sus efectos en los mercados, las empresas y el acceso a los recursos naturales y la tierra, corresponding to the research of  Sa","award":["HAR2016-75010-R"],"award-info":[{"award-number":["HAR2016-75010-R"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Processes"],"abstract":"<jats:p>This paper proposes a machine learning approach dealing with genetic programming to build classifiers through logical rule induction. In this context, we define and test a set of mutation operators across from different clinical datasets to improve the performance of the proposal for each dataset. The use of genetic programming for rule induction has generated interesting results in machine learning problems. Hence, genetic programming represents a flexible and powerful evolutionary technique for automatic generation of classifiers. Since logical rules disclose knowledge from the analyzed data, we use such knowledge to interpret the results and filter the most important features from clinical data as a process of knowledge discovery. The ultimate goal of this proposal is to provide the experts in the data domain with prior knowledge (as a guide) about the structure of the data and the rules found for each class, especially to track dichotomies and inequality. The results reached by our proposal on the involved datasets have been very promising when used in classification tasks and compared with other methods.<\/jats:p>","DOI":"10.3390\/pr8121565","type":"journal-article","created":{"date-parts":[[2020,11,28]],"date-time":"2020-11-28T03:51:16Z","timestamp":1606535476000},"page":"1565","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Genetic Programming Strategy to Induce Logical Rules for Clinical Data Analysis"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9452-1477","authenticated-orcid":false,"given":"Jos\u00e9 A.","family":"Castellanos-Garz\u00f3n","sequence":"first","affiliation":[{"name":"Department of Computer Science and Automatic, Faculty of Sciences, BISITE Research Group, University of Salamanca, Plaza de los Ca\u00eddos, s\/n, 37008 Salamanca, Spain"},{"name":"CISUC, Department of Computer Engineering, ECOS Research Group, University of Coimbra, P\u00f3lo II - Pinhal de Marrocos, 3030-290 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4813-4043","authenticated-orcid":false,"given":"Yeray","family":"Mezquita Mart\u00edn","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Automatic, Faculty of Sciences, BISITE Research Group, University of Salamanca, Plaza de los Ca\u00eddos, s\/n, 37008 Salamanca, Spain"}]},{"given":"Jos\u00e9 Luis","family":"Jaimes S\u00e1nchez","sequence":"additional","affiliation":[{"name":"Instituto Universitario de Estudios de la Ciencia y la Tecnolog\u00eda, University of Salamanca, 37008 Salamanca, Spain"}]},{"given":"Santiago Manuel","family":"L\u00f3pez Garc\u00eda","sequence":"additional","affiliation":[{"name":"Instituto Universitario de Estudios de la Ciencia y la Tecnolog\u00eda, University of Salamanca, 37008 Salamanca, Spain"}]},{"given":"Ernesto","family":"Costa","sequence":"additional","affiliation":[{"name":"CISUC, Department of Computer Engineering, ECOS Research Group, University of Coimbra, P\u00f3lo II - Pinhal de Marrocos, 3030-290 Coimbra, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,27]]},"reference":[{"unstructured":"Bandyopadhyay, S., and Pal, S.K. 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