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Article

A Genetic Programming Strategy to Induce Logical Rules for Clinical Data Analysis

1
Department of Computer Science and Automatic, Faculty of Sciences, BISITE Research Group, University of Salamanca, Plaza de los Caídos, s/n, 37008 Salamanca, Spain
2
CISUC, Department of Computer Engineering, ECOS Research Group, University of Coimbra, Pólo II - Pinhal de Marrocos, 3030-290 Coimbra, Portugal
3
Instituto Universitario de Estudios de la Ciencia y la Tecnología, University of Salamanca, 37008 Salamanca, Spain
*
Author to whom correspondence should be addressed.
Processes 2020, 8(12), 1565; https://doi.org/10.3390/pr8121565
Received: 31 October 2020 / Revised: 18 November 2020 / Accepted: 26 November 2020 / Published: 27 November 2020
(This article belongs to the Special Issue Bioinformatics Applications Based On Machine Learning)
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. View Full-Text
Keywords: clinical data; feature selection; genetic programming; machine learning; data mining; evolutionary computation clinical data; feature selection; genetic programming; machine learning; data mining; evolutionary computation
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MDPI and ACS Style

Castellanos-Garzón, J.A.; Mezquita Martín, Y.; Jaimes Sánchez, J.L.; López García, S.M.; Costa, E. A Genetic Programming Strategy to Induce Logical Rules for Clinical Data Analysis. Processes 2020, 8, 1565. https://doi.org/10.3390/pr8121565

AMA Style

Castellanos-Garzón JA, Mezquita Martín Y, Jaimes Sánchez JL, López García SM, Costa E. A Genetic Programming Strategy to Induce Logical Rules for Clinical Data Analysis. Processes. 2020; 8(12):1565. https://doi.org/10.3390/pr8121565

Chicago/Turabian Style

Castellanos-Garzón, José A., Yeray Mezquita Martín, José Luis Jaimes Sánchez, Santiago Manuel López García, and Ernesto Costa. 2020. "A Genetic Programming Strategy to Induce Logical Rules for Clinical Data Analysis" Processes 8, no. 12: 1565. https://doi.org/10.3390/pr8121565

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